README.md

ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation

The official implementation of Shape-aware Convolutional Layer.

ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation

Jinming Cao, Hanchao Leng, Dani Lischinski, Danny Cohen-Or, Changhe Tu, Yangyan Li, ICCV 2021.

Introduction

We design a Shape-aware Convolutional(ShapeConv) layer to explicitly model the shape information for enhancing the RGB-D semantic segmentation accuracy. Specifically, we decompose the depth feature into a shape-component and a base-component, after which two learnable weights are introduced to handle the shape and base with differentiation.

Usage

Installation

1. Requirements

• Linux
• Python 3.6+
• PyTorch 1.7.0 or higher
• CUDA 10.0 or higher

We have tested the following versions of OS and softwares:

• OS: Ubuntu 16.04.6 LTS
• CUDA: 10.0
• PyTorch 1.7.0
• Python 3.6.9

2. Install dependencies.

pip install -r requirements.txt

Dataset

Download the offical dataset and convert to a format appropriate for this project. See here.

Or download the converted dataset:

• NYU-V2

Evaluation

1. Model

   Download trained model and put it in folder ./model_zoo. See all trained models here.

2. Config

   Edit config file in ./configs. The config files in ./configs correspond to the model files in ./model_zoo.

   1. Set inference.gpu_id = CUDA_VISIBLE_DEVICES. CUDA_VISIBLE_DEVICES is used to specify which GPUs should be visible to a CUDA application, e.g., inference.gpu_id = "0,1,2,3".
   2. Set dataset_root = path_to_dataset. path_to_dataset represents the path of dataset. e.g.,dataset_root = "/home/shape_conv/nyu_v2".

3. Run

   1. Distributed evaluation, please run:

   ./tools/dist_test.sh config_path checkpoint_path gpu_num

   • config_path is path of config file;
   • checkpoint_pathis path of model file;
   • gpu_num is the number of GPUs used, note that gpu_num <= len(inference.gpu_id).

   E.g., evaluate shape-conv model on NYU-V2(40 categories), please run:

   ./tools/dist_test.sh configs/nyu/nyu40_deeplabv3plus_resnext101_shape.py model_zoo/nyu40_deeplabv3plus_resnext101_shape.pth 4

   2. Non-distributed evaluation

   python tools/test.py config_path checkpoint_path

Train

1. Config

   Edit config file in ./configs.

   1. Set inference.gpu_id = CUDA_VISIBLE_DEVICES.

      E.g.,inference.gpu_id = "0,1,2,3".

   2. Set dataset_root = path_to_dataset.

      E.g.,dataset_root = "/home/shape_conv/nyu_v2".

2. Run

   1. Distributed training

   ./tools/dist_train.sh config_path gpu_num

   E.g., train shape-conv model on NYU-V2(40 categories) with 4 GPUs, please run:

   ./tools/dist_train.sh configs/nyu/nyu40_deeplabv3plus_resnext101_shape.py 4

   2. Non-distributed training

   python tools/train.py config_path

Result

For more result and pre-trained model, please see model zoo.

NYU-V2(40 categories)

Architecture	Backbone	MS & Flip	Shape Conv	mIOU
DeepLabv3plus	ResNeXt-101	False	False	48.9%
DeepLabv3plus	ResNeXt-101	False	True	50.2%
DeepLabv3plus	ResNeXt-101	True	False	50.3%
DeepLabv3plus	ResNeXt-101	True	True	51.3%

SUN-RGBD

Architecture	Backbone	MS & Flip	Shape Conv	mIOU
DeepLabv3plus	ResNet-101	False	False	46.9%
DeepLabv3plus	ResNet-101	False	True	47.6%
DeepLabv3plus	ResNet-101	True	False	47.6%
DeepLabv3plus	ResNet-101	True	True	48.6%

SID(Stanford Indoor Dataset)

Architecture	Backbone	MS & Flip	Shape Conv	mIOU
DeepLabv3plus	ResNet-101	False	False	54.55%
DeepLabv3plus	ResNet-101	False	True	60.6%

Citation

If you find this repo useful, please consider citing:

@article{cao2021shapeconv,
  title={ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation},
  author={Cao, Jinming and Leng, Hanchao and Lischinski, Dani and Cohen-Or, Danny and Tu, Changhe and Li, Yangyan},
  journal={arXiv preprint arXiv:2108.10528},
  year={2021}
}

Acknowledgments

This repository is heavily based on vedaseg.