3D Bounding Box Estimation Using Deep Learning and Geometry

Introduction

This repo is PyTorch implementation for this paper. In this paper, they collect KITTI 2D Object Dataset and introduce a flow to estimate object pose and dimension. If you are looking for TensorFlow implementation, here is a great repo.

Dependency

• numpy
• opencv
• yaml
• PyTorch
• torchvision
• CUDA

Usage

Before using this code, you need download data from KITTI and unzip it. After that, you need to add the kitti path of dataset to config.yaml.

kitti_path: somewhere # Root of kitti, where contrain trainning/ and testing/   

Also, you can set up parameters for training and weight of loss as describded in paper.

epochs: 8 # How many epoch for training?
bins: 2  # How many bins you want to split?
w: 0.8
alpha: 0.8
batches: 8             

After setting up, just type it for training

python Train.py

It will store model in ./models. For simple evaluation, type

python Eval.py

This will calculate average orientation and dimension error (in degree and meters).

Reference

• 3D Bounding Box Estimation Using Deep Learning and Geometry
• PyTorch
• KITTI