[Documentation] Nvidia style edits to surgical scene reconstruction #1329
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Signed-off-by: meg miranda <[email protected]>
Greptile SummaryThis PR applies NVIDIA style guide edits to the surgical scene reconstruction README, significantly improving documentation structure and clarity. Major Changes:
Issues Found:
Confidence Score: 4/5
Important Files Changed
Sequence DiagramsequenceDiagram
participant User
participant README
participant Dataset
participant Application
User->>README: Read Quick Start
README->>User: Step 1: Clone HoloHub
README->>User: Step 2: Agree to Terms
User->>Dataset: Download EndoNeRF Dataset
README->>User: Step 3: Run Training
User->>Application: ./holohub run surgical_scene_recon train
Application->>Application: Load RGB, depth, masks, poses
Application->>Application: Initialize multi-frame point cloud
Application->>Application: Coarse stage (static Gaussians)
Application->>Application: Fine stage (temporal deformation)
Application->>User: Training complete
README->>User: Step 4: Run Rendering
User->>Application: ./holohub run surgical_scene_recon render
Application->>Application: Load checkpoint
Application->>Application: Apply temporal deformation
Application->>User: Real-time 3D visualization
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Note Other AI code review bot(s) detectedCodeRabbit has detected other AI code review bot(s) in this pull request and will avoid duplicating their findings in the review comments. This may lead to a less comprehensive review. WalkthroughRewrote and reorganized the Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~12 minutes Suggested reviewers
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Actionable comments posted: 1
🤖 Fix all issues with AI agents
In @applications/surgical_scene_recon/README.md:
- Line 23: The README contains a grammatical typo: change the phrase "uses
cases" to "use cases" on the affected sentence (line with "It is ideal for uses
cases, such as:") so it reads "It is ideal for use cases, such as:"; update that
exact wording in applications/surgical_scene_recon/README.md.
📜 Review details
Configuration used: Organization UI
Review profile: CHILL
Plan: Pro
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applications/surgical_scene_recon/README.md
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- GitHub Check: Greptile Review
🔇 Additional comments (3)
applications/surgical_scene_recon/README.md (3)
3-5: Clarify intent of the two opening paragraphs.Lines 3 and 5 now convey nearly identical information—both describe real-time 3D surgical reconstruction with Gaussian Splatting and temporal deformation. After restructuring to emphasize the end-to-end pipeline, one of these appears redundant.
Verify whether both paragraphs serve a distinct purpose (e.g., detailed vs. summary) or if one should be removed for clarity.
96-107: Dataset setup instructions are clear and complete.The restructured dataset setup (lines 96–107) with numbered steps, inline code blocks, and explicit path placeholders is an improvement. The symlink warning on Line 109 is important context that is appropriately positioned.
41-41: All external links and documentation references in the README are properly formatted and valid. The relative path to the HoloHub glossary (../../README.md#Glossary) is correct, and the glossary section exists in the root README. The file appropriately references and uses HoloHub glossary terms for project-specific locations as required.
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line 120/126 the title of Model. What model are we talking here?
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That model refers to the below, if this description helps maybe you could frame it the way you like @megnvidia ?
The surgical_scene_recon application uses a 4D Dynamic Gaussian Splatting model that combines:
- 3D Gaussian Splatting - A point-based neural scene representation
- HexPlane Temporal Deformation Network - A spatiotemporal feature grid with MLPs for modeling tissue dynamics
…to the training section, the duplication bothered me Signed-off-by: meg miranda <[email protected]>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Signed-off-by: megnvidia <[email protected]>
![greptile-apps[bot]](https://avatars.githubusercontent.com/in/867647?s=60&v=4){kind=small}
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Greptile Overview
Greptile Summary
This PR applies NVIDIA style guide edits to the surgical scene reconstruction README, improving formatting, organization, and readability.
Key improvements:
- Enhanced document structure with clearer section headings and better organization
- Improved Quick Start instructions with numbered lists and better code block formatting
- Standardized terminology and punctuation throughout
- Added more descriptive section titles (e.g., "Pulling Soft Tissues Dataset" instead of "Data")
- Refined technical descriptions and explanations
Issues found:
- Line 23: Typo "uses cases" should be "use cases"
- Line 52: Inaccurate term "virtual surgical training" should be "model training"
- Line 143: Incorrect technical description of Temporal Deformation Network references MRI data interpolation instead of surgical tissue deformation
- Line 5: Redundant sentence that duplicates the introduction
- Line 135: Inconsistent capitalization of "Gaussian"
Confidence Score: 3/5
- This PR has some style improvements but contains a critical technical inaccuracy and several smaller issues that should be fixed
- Score reflects that while most changes are beneficial style edits, there is a significant technical error (incorrect TGN model description on line 143), a typo, and inaccurate terminology that could confuse users
- The README.md file needs attention, specifically line 143 which contains incorrect technical information about the Temporal Deformation Network
Important Files Changed
File Analysis
| Filename | Score | Overview |
|---|---|---|
| applications/surgical_scene_recon/README.md | 3/5 | Style guide edits to improve clarity and consistency; found typo, inaccurate terminology, and incorrect technical description |
Sequence Diagram
sequenceDiagram
participant Author as PR Author
participant File as README.md
participant Review as Documentation Review
participant User as End User
Author->>File: Apply NVIDIA style guide edits
Author->>File: Restructure sections and headings
Author->>File: Improve Quick Start formatting
Author->>File: Enhance dataset documentation
Author->>File: Standardize terminology
Note over File: Introduced typo: "uses cases"
Note over File: Added inaccurate term: "virtual surgical training"
Note over File: Added incorrect TGN description
Note over File: Redundant sentence on line 5
Review->>File: Identify style improvements
Review->>File: Detect typo on line 23
Review->>File: Find terminology issue on line 52
Review->>File: Catch technical error on line 143
Review->>File: Note inconsistencies
File->>User: Improved readability & organization
File->>User: Better Quick Start instructions
File->>User: Clearer dataset setup steps
Note over Review: Requires fixes before merge
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Greptile Overview
Greptile Summary
This PR applies NVIDIA style guide edits to the surgical scene reconstruction README, improving formatting, organization, and readability throughout the documentation.
Major changes:
- Restructured introduction section with clearer feature descriptions
- Enhanced Quick Start section with numbered lists and better command formatting
- Improved dataset documentation with detailed download and setup instructions
- Standardized section headings and terminology throughout
- Reformatted code blocks and citation sections for consistency
Issues found:
- Line 143 contains a critical technical inaccuracy: describes the Temporal Deformation Network as a "Temporal Generative Network (TGN)" for "missing MRI data," which is incorrect for this surgical endoscopy application
- Line 5 introduces redundant content that duplicates line 3
- Inconsistent code block indentation in Step 3 (line 54)
- Terminology errors: "3D Gaussian" should be "3D Gaussians" (line 135), "Gaussian functions" should be "Gaussians" (line 243)
Confidence Score: 3/5
- This PR requires revisions before merging due to technical inaccuracies in the documentation
- Score of 3 reflects that while most style improvements are beneficial, there is a critical technical error at line 143 that misrepresents the Temporal Deformation Network functionality, terminology errors that could confuse readers, and formatting inconsistencies that should be addressed before merging
- Pay close attention to line 143 (incorrect TGN description), line 135 (terminology), and line 243 (terminology)
Important Files Changed
File Analysis
| Filename | Score | Overview |
|---|---|---|
| applications/surgical_scene_recon/README.md | 3/5 | Style guide improvements with some formatting inconsistencies and one critical technical inaccuracy about the Temporal Deformation Network (line 143) |
Sequence Diagram
sequenceDiagram
participant User
participant PR as Pull Request Review
participant Doc as README.md
participant StyleGuide as NVIDIA Style Guide
User->>PR: Submit style edits PR
PR->>Doc: Apply formatting changes
Doc->>Doc: Restructure sections
Doc->>Doc: Improve Quick Start formatting
Doc->>Doc: Standardize terminology
Doc->>Doc: Enhance readability
PR->>StyleGuide: Validate against style guide
StyleGuide-->>PR: Check formatting consistency
StyleGuide-->>PR: Check technical accuracy
Note over Doc: Issues found:<br/>- Redundant line 5<br/>- Inconsistent indentation<br/>- Inaccurate TGN description<br/>- Terminology errors
PR->>User: Return review feedback
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Actionable comments posted: 1
🤖 Fix all issues with AI agents
In @applications/surgical_scene_recon/README.md:
- Around line 126-150: The opening sentence of the Temporal Deformation Network
(TGN) description incorrectly references MRI data; update the sentence that
currently reads "Temporal Generative Network (TGN) model generates data that
integrates bidirectional deformation estimation with temporal prediction to
interpolate missing MRI data and forecast future frames" to instead state that
the TGN interpolates missing endoscopic frames and forecasts future frames using
EndoNeRF endoscopic data (RGB images + stereo depth + camera poses), keeping the
rest of the TGN details (HexPlane 4D grid, MLP decoder,
input/output/training/inference) unchanged.
🧹 Nitpick comments (1)
applications/surgical_scene_recon/README.md (1)
156-157: Minor grammar refinement: "where" clause construction.Lines 156–157 use grammatically awkward constructions:
- "The Coarse Stage where the application learns..."
- "The Fine Stage where a temporal deformation network model is added..."
These read more naturally as "In the Coarse Stage, the application learns..." or "The Coarse Stage: the application learns..."
This is a minor stylistic improvement; the meaning is clear as written.
✏️ Proposed improvements
- 1. The Coarse Stage where the application learns the base static Gaussian models without deformation. - 2. The Fine Stage where a temporal deformation network model is added for dynamic tissue modeling. + 1. **Coarse Stage:** The application learns the base static Gaussian models without deformation. + 2. **Fine Stage:** A temporal deformation network model is added for dynamic tissue modeling.
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Review profile: CHILL
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applications/surgical_scene_recon/README.md
🔇 Additional comments (2)
applications/surgical_scene_recon/README.md (2)
12-28: Past comment resolved: Use cases typo fixed.Line 23 correctly shows "use cases" (was flagged as "uses cases" in previous review). Feature additions ("Tool Removal," "End-to-End Training") and restructured use cases are clear and well-organized.
131-139: Past comment resolved: "Degree 3" terminology clarified.Line 138 now explains "Degree 3 (16 coefficients per gaussian for view-dependent color)" which directly addresses the previous comment asking "what is 'degree 3'?" The explanation is clear and technical.
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My intuition is telling me that the later half of this README has some content that we might not really care to keep, but I can't make those choices without discussion and feedback from a subject matter expert. I did more formatting changes on this article than I had originally intended. These readme's span the gray are between user guide and readme content. I probably leaned more toward user guide formatting style with this. We may not want that. |
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Signed-off-by: megnvidia <[email protected]>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Signed-off-by: megnvidia <[email protected]>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Signed-off-by: megnvidia <[email protected]>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Signed-off-by: megnvidia <[email protected]>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Signed-off-by: megnvidia <[email protected]>
| ./holohub run surgical_scene_recon train | ||
| ``` | ||
| ./holohub run surgical_scene_recon train | ||
| ``` |
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inconsistent indentation - code block is indented but other code blocks in Quick Start section are not
| ./holohub run surgical_scene_recon train | |
| ``` | |
| ./holohub run surgical_scene_recon train | |
| ``` | |
| ```bash | |
| ./holohub run surgical_scene_recon train |
Note: If this suggestion doesn't match your team's coding style, reply to this and let me know. I'll remember it for next time!
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Greptile Overview
Greptile Summary
This PR applies style guide edits to the surgical scene reconstruction README, improving formatting, organization, and readability. However, it introduces a critical factual error in the Temporal Deformation Network description.
Key Issues:
- Critical: Line 142 contains incorrect description stating the model "integrates bidirectional deformation estimation with temporal prediction to interpolate missing MRI data and forecast future frames" - this application uses surgical endoscopy video for tissue reconstruction, not MRI data
- Terminology inconsistency: Line 134 changed "3D Gaussians" (plural) to "3D Gaussian" (singular), but the architecture uses multiple Gaussian primitives (the original plural form was correct)
Positive Changes:
- Improved section organization and heading structure
- Better formatting with numbered lists and consistent indentation
- Clearer Quick Start instructions with step-by-step guidance
- Enhanced dataset setup documentation with verification steps
- More detailed training pipeline explanation
Confidence Score: 2/5
- This PR has a critical factual error that misrepresents the application's functionality and must be corrected before merging
- Score reflects one critical factual error (TGN description mentions MRI instead of surgical video) and one terminology inconsistency (Gaussians plural→singular). While the style improvements are beneficial, the factual inaccuracy about the model's purpose is misleading and needs correction
- applications/surgical_scene_recon/README.md requires attention - specifically line 142 (critical factual error) and line 134 (terminology correction)
Important Files Changed
File Analysis
| Filename | Score | Overview |
|---|---|---|
| applications/surgical_scene_recon/README.md | 2/5 | Style guide edits to README with one critical factual error about Temporal Deformation Network (mentions MRI instead of surgical video) and one terminology inconsistency (singular vs plural Gaussians) |
Sequence Diagram
sequenceDiagram
participant User
participant PR as Pull Request
participant README as README.md
participant Review as Code Review
User->>PR: Submit style guide edits
PR->>README: Apply formatting changes
Note over README: Reorganize sections<br/>Update headings<br/>Improve clarity
PR->>README: Update model descriptions
Note over README: ⚠️ Incorrect TGN description<br/>(MRI vs surgical video)
PR->>README: Standardize terminology
Note over README: ⚠️ Change "Gaussians" to "Gaussian"<br/>(plural to singular)
Review->>README: Identify factual error
Review->>README: Identify terminology issue
Review->>User: Request corrections
| ### Temporal Deformation Network | ||
| - Temporal Deformation Network model | ||
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Description incorrect - mentions MRI data and forecast future frames, but this application uses surgical endoscopy video for tissue reconstruction, not MRI
| The Temporal Deformation Network enables dynamic scene modeling by deforming the base Gaussian representations over time to accurately capture tissue movement and deformation during surgery. |
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Signed-off-by: megnvidia <[email protected]>
Signed-off-by: meg miranda <[email protected]>
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Greptile Overview
Greptile Summary
This PR applies NVIDIA style guide edits to the surgical scene reconstruction README, improving documentation clarity and structure.
Key Changes:
- Restructured introduction to lead with application purpose
- Expanded Quick Start section with numbered steps and clearer instructions
- Enhanced dataset download and setup instructions with better formatting
- Added detailed training pipeline documentation
- Standardized formatting for citations and code blocks
- Improved section organization (e.g., "Data" → "Pulling Soft Tissues Dataset")
Issue Found:
- Line 54-55: Duplicate command
./holohub run surgical_scene_recon trainwith incorrect indentation in the training code block
Confidence Score: 4/5
- This PR is safe to merge after fixing the duplicate command in the training instructions
- The PR contains style guide improvements to documentation with good restructuring and enhanced clarity. However, there is one syntax error (duplicate command line 54-55) that needs correction. The changes are documentation-only with no code logic changes, making this low-risk overall.
- applications/surgical_scene_recon/README.md requires attention to fix the duplicate command in Step 3
Important Files Changed
File Analysis
| Filename | Score | Overview |
|---|---|---|
| applications/surgical_scene_recon/README.md | 4/5 | Style guide updates with one syntax error: duplicate command in code block (line 54-55) |
Sequence Diagram
sequenceDiagram
participant Dev as Developer
participant PR as Pull Request
participant Doc as README.md
participant User as End User
Dev->>Doc: Apply NVIDIA style guide edits
Dev->>Doc: Restructure sections for clarity
Dev->>Doc: Expand Quick Start instructions
Dev->>Doc: Add detailed training pipeline info
Dev->>Doc: Standardize formatting and citations
Note over Dev,Doc: Duplicate command added by mistake
Dev->>PR: Submit changes
PR->>User: Documentation improvements available
User->>Doc: Read enhanced documentation
User->>Doc: Follow improved instructions
| 1. Read and agree to the [Terms and Conditions](https://docs.google.com/document/d/1P6q2hXoGpVMKeD-PpjYYdZ0Yx1rKZdJF1rXxpobbFMY/edit?usp=share_link) for the EndoNeRF dataset. | ||
| 1. EndoNeRF sample dataset is being downloaded automatically when building the application. | ||
| 1. Optionally, for manual download of the dataset, refer to the [Data](#pulling-soft-tissues-dataset) section below. | ||
| 1. Optionally, if you do not agree to the terms and conditions, set the `HOLOHUB_DOWNLOAD_DATASETS` environment variable to `OFF` and manually download the dataset and place it in the correct location by following the instructions in the [Data](#pulling-soft-tissues-dataset) section below. |
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| 1. Read and agree to the [Terms and Conditions](https://docs.google.com/document/d/1P6q2hXoGpVMKeD-PpjYYdZ0Yx1rKZdJF1rXxpobbFMY/edit?usp=share_link) for the EndoNeRF dataset. | |
| 1. EndoNeRF sample dataset is being downloaded automatically when building the application. | |
| 1. Optionally, for manual download of the dataset, refer to the [Data](#pulling-soft-tissues-dataset) section below. | |
| 1. Optionally, if you do not agree to the terms and conditions, set the `HOLOHUB_DOWNLOAD_DATASETS` environment variable to `OFF` and manually download the dataset and place it in the correct location by following the instructions in the [Data](#pulling-soft-tissues-dataset) section below. | |
| 1. Read and agree to the [Terms and Conditions](https://docs.google.com/document/d/1P6q2hXoGpVMKeD-PpjYYdZ0Yx1rKZdJF1rXxpobbFMY/edit?usp=share_link) for the EndoNeRF dataset. | |
| 2. EndoNeRF sample dataset is being downloaded automatically when building the application. | |
| 3. Optionally, for manual download of the dataset, refer to the [Data](#pulling-soft-tissues-dataset) section below. | |
| 4. Optionally, if you do not agree to the terms and conditions, set the `HOLOHUB_DOWNLOAD_DATASETS` environment variable to `OFF` and manually download the dataset and place it in the correct location by following the instructions in the [Data](#pulling-soft-tissues-dataset) section below. |
It would be great if we can make them readable for people who read the raw markdown instead of the rendered version.
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I am going to continue to forget this....but i will work on it for y'all
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| ## Data | ||
| ## Pulling Soft Tissues Dataset |
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Isn't better to keep Data section instead of making it specific? Users might not know what "Pulling Soft Tissues" is but they would look for a "Data" secton to know what data to use.
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it goes against all my training as a tech writer at so many different companies
Think my IBM editor had 'no headings without supporting content' upon pain of a message to management kind of editing habit. Basically a heading with no words explaining why it is there is sort of like having a skin tag, irritating and easy to cut off.
you are probably going to have to debate/fight me a bit on this or give in to some of my other wild editing ideas.
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How about Dataset: Soft Tissue Pulling
| 1. In the Google Drive folder, you'll see: | ||
| **Alternative:** Visit the [EndoNeRF repository](https://github.com/med-air/EndoNeRF) | ||
| - `cutting_tissues_twice` | ||
| - `pulling_soft_tissues` | ||
| 1. Download `pulling_soft_tissues`. | ||
| * Visit the [EndoNeRF repository](https://github.com/med-air/EndoNeRF). |
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| 1. In the Google Drive folder, you'll see: | |
| **Alternative:** Visit the [EndoNeRF repository](https://github.com/med-air/EndoNeRF) | |
| - `cutting_tissues_twice` | |
| - `pulling_soft_tissues` | |
| 1. Download `pulling_soft_tissues`. | |
| * Visit the [EndoNeRF repository](https://github.com/med-air/EndoNeRF). | |
| 1. In the Google Drive folder, you'll see: | |
| - `cutting_tissues_twice` | |
| - `pulling_soft_tissues` | |
| 2. Download `pulling_soft_tissues`. | |
| * Visit the [EndoNeRF repository](https://github.com/med-air/EndoNeRF). |
Here and elsewhere, please ensure the raw markdown makes sense for the enumerated lists. I know they would be rendered correctly but many people would read the raw code.
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changing locally
| ``` | ||
| ## Model | ||
| ## Models Used by the `surgical_scene_recon` Application |
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Can we keep the section as "Model"? Isn't it trivial that the Model is for surgical_scene_recon Application as this README is specific to surgical_scene_recon Application?
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also another longstanding tech writer training thing. I get pushed to make sure headings are descriptive if they have to stand alone. Some of it comes from the way search engines work. And the way web indexers crawl the web.
bhashemian
changed the title
Signed-off-by: meg miranda <[email protected]>
| - If you do not agree to the terms and conditions, set the `HOLOHUB_DOWNLOAD_DATASETS` environment variable to `OFF` and manually download the dataset and place it in the correct location by following the instructions in the [Data](#data) section below. | ||
| 1. Read and agree to the [Terms and Conditions](https://docs.google.com/document/d/1P6q2hXoGpVMKeD-PpjYYdZ0Yx1rKZdJF1rXxpobbFMY/edit?usp=share_link) for the EndoNeRF dataset. | ||
| 2. EndoNeRF sample dataset is being downloaded automatically when building the application. | ||
| 3. Optionally, for manual download of the dataset, refer to the [Data](#pulling-soft-tissues-dataset) section below. |
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syntax: Broken anchor link - section is named "Obtaining the Pulling Soft Tissues Dataset" but anchor references #pulling-soft-tissues-dataset
| 3. Optionally, for manual download of the dataset, refer to the [Data](#pulling-soft-tissues-dataset) section below. | |
| 3. Optionally, for manual download of the dataset, refer to the [Data](#obtaining-the-pulling-soft-tissues-dataset) section below. |
| 1. Read and agree to the [Terms and Conditions](https://docs.google.com/document/d/1P6q2hXoGpVMKeD-PpjYYdZ0Yx1rKZdJF1rXxpobbFMY/edit?usp=share_link) for the EndoNeRF dataset. | ||
| 2. EndoNeRF sample dataset is being downloaded automatically when building the application. | ||
| 3. Optionally, for manual download of the dataset, refer to the [Data](#pulling-soft-tissues-dataset) section below. | ||
| 4. Optionally, if you do not agree to the terms and conditions, set the `HOLOHUB_DOWNLOAD_DATASETS` environment variable to `OFF` and manually download the dataset and place it in the correct location by following the instructions in the [Data](#pulling-soft-tissues-dataset) section below. |
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syntax: Same broken anchor link as line 42
| 4. Optionally, if you do not agree to the terms and conditions, set the `HOLOHUB_DOWNLOAD_DATASETS` environment variable to `OFF` and manually download the dataset and place it in the correct location by following the instructions in the [Data](#pulling-soft-tissues-dataset) section below. | |
| 4. Optionally, if you do not agree to the terms and conditions, set the `HOLOHUB_DOWNLOAD_DATASETS` environment variable to `OFF` and manually download the dataset and place it in the correct location by following the instructions in the [Data](#obtaining-the-pulling-soft-tissues-dataset) section below. |
| - **Output:** Real-time 3D tissue reconstruction without surgical instruments | ||
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| ### Use Cases | ||
| It is ideal for use cases, such as: |
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syntax: Remove comma after "such as" - incorrect punctuation
| It is ideal for use cases, such as: | |
| It is ideal for use cases such as: |
| - **Production Ready:** Tested and optimized Holoscan pipeline with complete Docker containerization | ||
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| ### What It Does | ||
| It takes input from EndoNeRF surgical datasets (RGB images + stereo depth + camera poses + tool masks). It processes the input using multi-frame Gaussian Splatting with a 4D spatiotemporal deformation network. And it outputs real-time 3D tissue reconstruction without surgical instruments. |
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style: Awkward sentence structure with fragmented clauses separated by periods. Consider combining for better flow:
| It takes input from EndoNeRF surgical datasets (RGB images + stereo depth + camera poses + tool masks). It processes the input using multi-frame Gaussian Splatting with a 4D spatiotemporal deformation network. And it outputs real-time 3D tissue reconstruction without surgical instruments. | |
| The application takes input from EndoNeRF surgical datasets (RGB images + stereo depth + camera poses + tool masks), processes it using multi-frame Gaussian Splatting with a 4D spatiotemporal deformation network, and outputs real-time 3D tissue reconstruction without surgical instruments. |
Note: If this suggestion doesn't match your team's coding style, reply to this and let me know. I'll remember it for next time!
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Actionable comments posted: 1
🤖 Fix all issues with AI agents
In `@applications/surgical_scene_recon/README.md`:
- Around line 42-43: The README contains broken anchor links using
'#pulling-soft-tissues-dataset'; update both occurrences in the listed items to
the correct anchor that matches the actual heading "Obtaining the Pulling Soft
Tissues Dataset" (use '#obtaining-the-pulling-soft-tissues-dataset' or the
repository's generated slug for that heading) so the links point to the real
section.
🧹 Nitpick comments (2)
applications/surgical_scene_recon/README.md (2)
129-147: Consider standardizing bullet point style for consistency.The model descriptions use mixed styles—some bullets are key-value pairs ("Architecture: X"), while others are sentence fragments ("Spherical Harmonics of degree 3"). While technically clear, standardizing to one style would improve readability.
📋 Example standardization
Option 1: All as key-value pairs
- Architecture: 3D Gaussians with learned position, scale, rotation, opacity, and color - Initialization: Multi-frame point cloud (~30,000-50,000 points from all frames) - Renderer: `gsplat` library (CUDA-accelerated differentiable rasterization) - Color Representation: Spherical Harmonics of degree 3 (16 coefficients per Gaussian) - Resolution: 640×512 pixels (RGB, three channels)Option 2: All as complete sentences
- Uses 3D Gaussians with learned position, scale, rotation, opacity, and color - Initializes from multi-frame point cloud (~30,000-50,000 points from all frames) - Renders using `gsplat` library (CUDA-accelerated differentiable rasterization) - Employs Spherical Harmonics of degree 3 (16 coefficients per Gaussian for view-dependent color) - Outputs at 640×512 pixel resolution (RGB, three channels)
163-171: Simplify the training pipeline list for better readability.The training pipeline steps (lines 165-171) have some awkward phrasing and inconsistent grammar. Consider rephrasing for clarity and consistency.
✍️ Proposed refinement
-The **training pipeline** (`gsplat_train.py`) runs in the following order: - -1. Data Loading using EndoNeRF parser loads RGB, depth, masks, and poses. -2. Initialization uses Multi-frame point cloud (~30k points). -3. Training happens in two stages: - - Coarse - - Fine -4. Optimization is done by the Adam (Adaptive Moment Estimation) optimizer with batch-size scaled learning rates. -5. Regularization, for depth loss, TV loss, and masking losses, is performed on the data. +The **training pipeline** (`gsplat_train.py`) runs in the following order: + +1. **Data Loading:** EndoNeRF parser loads RGB, depth, masks, and poses. +2. **Initialization:** Multi-frame point cloud (~30k points). +3. **Two-Stage Training:** + - Coarse stage (static Gaussians) + - Fine stage (with deformation) +4. **Optimization:** Adam optimizer with batch-size scaled learning rates. +5. **Regularization:** Depth loss, TV loss, and masking losses.
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Repo: nvidia-holoscan/holohub PR: 0
File: CONTRIBUTING.md:0-0
Timestamp: 2025-12-18T16:19:45.117Z
Learning: Applies to **/(@(operators|applications|workflows|gxf_extensions))/**/README.md : Every application and operator must include a comprehensive README.md file with purpose, usage, requirements, examples, and architecture information
📚 Learning: 2025-12-18T16:19:45.117Z
Learnt from: CR
Repo: nvidia-holoscan/holohub PR: 0
File: CONTRIBUTING.md:0-0
Timestamp: 2025-12-18T16:19:45.117Z
Learning: Applies to **/(@(operators|applications|workflows|gxf_extensions|tutorials))/**/README.md : README.md should use HoloHub glossary terms when referring to project-specific locations and concepts
Applied to files:
applications/surgical_scene_recon/README.md
🪛 markdownlint-cli2 (0.18.1)
applications/surgical_scene_recon/README.md
42-42: Link fragments should be valid
(MD051, link-fragments)
43-43: Link fragments should be valid
(MD051, link-fragments)
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🔇 Additional comments (3)
applications/surgical_scene_recon/README.md (3)
1-28: Strong technical introduction with comprehensive feature coverage.The rewritten introduction effectively emphasizes the key technologies (Holoscan SDK, 3D Gaussian Splatting, temporal deformation networks) and provides clear use cases. The feature list is well-structured and informative.
67-123: Excellent dataset documentation with clear setup instructions.The expanded dataset section provides comprehensive details including contents, download locations, setup steps, and structure verification. The warning about physical paths vs. symlinks (line 108) is crucial for Docker users and appropriately emphasized.
189-313: Well-structured technical documentation in the remaining sections.The pipeline architecture diagram, requirements, testing instructions, performance metrics, troubleshooting guide, and citations are all clearly presented and technically accurate. The troubleshooting section is particularly helpful with specific causes and solutions for common issues.
✏️ Tip: You can disable this entire section by setting review_details to false in your review settings.
| 3. Optionally, for manual download of the dataset, refer to the [Data](#pulling-soft-tissues-dataset) section below. | ||
| 4. Optionally, if you do not agree to the terms and conditions, set the `HOLOHUB_DOWNLOAD_DATASETS` environment variable to `OFF` and manually download the dataset and place it in the correct location by following the instructions in the [Data](#pulling-soft-tissues-dataset) section below. |
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Fix broken anchor links to match the actual section heading.
The anchor links #pulling-soft-tissues-dataset don't match the actual section heading "Obtaining the Pulling Soft Tissues Dataset" on line 67. This will result in broken navigation when users click these links.
🔗 Proposed fix
-3. Optionally, for manual download of the dataset, refer to the [Data](`#pulling-soft-tissues-dataset`) section below.
-4. Optionally, if you do not agree to the terms and conditions, set the `HOLOHUB_DOWNLOAD_DATASETS` environment variable to `OFF` and manually download the dataset and place it in the correct location by following the instructions in the [Data](`#pulling-soft-tissues-dataset`) section below.
+3. Optionally, for manual download of the dataset, refer to the [Data](`#obtaining-the-pulling-soft-tissues-dataset`) section below.
+4. Optionally, if you do not agree to the terms and conditions, set the `HOLOHUB_DOWNLOAD_DATASETS` environment variable to `OFF` and manually download the dataset and place it in the correct location by following the instructions in the [Data](`#obtaining-the-pulling-soft-tissues-dataset`) section below.🧰 Tools
🪛 markdownlint-cli2 (0.18.1)
42-42: Link fragments should be valid
(MD051, link-fragments)
43-43: Link fragments should be valid
(MD051, link-fragments)
🤖 Prompt for AI Agents
In `@applications/surgical_scene_recon/README.md` around lines 42 - 43, The README
contains broken anchor links using '#pulling-soft-tissues-dataset'; update both
occurrences in the listed items to the correct anchor that matches the actual
heading "Obtaining the Pulling Soft Tissues Dataset" (use
'#obtaining-the-pulling-soft-tissues-dataset' or the repository's generated slug
for that heading) so the links point to the real section.
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| - If you do not agree to the terms and conditions, set the `HOLOHUB_DOWNLOAD_DATASETS` environment variable to `OFF` and manually download the dataset and place it in the correct location by following the instructions in the [Data](#data) section below. | ||
| 1. Read and agree to the [Terms and Conditions](https://docs.google.com/document/d/1P6q2hXoGpVMKeD-PpjYYdZ0Yx1rKZdJF1rXxpobbFMY/edit?usp=share_link) for the EndoNeRF dataset. | ||
| 2. EndoNeRF sample dataset is being downloaded automatically when building the application. | ||
| 3. Optionally, for manual download of the dataset, refer to the [Data](#pulling-soft-tissues-dataset) section below. |
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syntax: Broken anchor link - section is "Obtaining the Pulling Soft Tissues Dataset" but anchor is #pulling-soft-tissues-dataset
| 3. Optionally, for manual download of the dataset, refer to the [Data](#pulling-soft-tissues-dataset) section below. | |
| 3. Optionally, for manual download of the dataset, refer to the [Data](#obtaining-the-pulling-soft-tissues-dataset) section below. |
3 participants
style guide edits to the surgical scene reconstruction readme.
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