SimpleDepthEstimation

Introduction

This is an unified codebase for NN-based monocular depth estimation methods, the framework is based on detectron2 (with a lot of modifications) and supports both supervised and self-supervised monocular depth estimation methods. The main goal for developing this repository is to help understand popular depth estimation papers, I tried my best to keep the code simple.

Environment:

1. clone this repo

   SDE_ROOT=/path/to/SimpleDepthEstimation
   git clone https://github.com/zzzxxxttt/SimpleDepthEstimation $SDE_ROOT
   cd $SDE_ROOT

2. create a new conda environment and activate it

   conda create -n sde python=3.6 
   conda activate sde

3. install torch==1.8.0 and torchvision==0.9.0 follow the official instructions. (I haven't tried other pytorch versions)
4. install other requirements

   pip install -r requirements.txt

Data preparation

KITTI:

Download and extract KITTI raw dataset, refined KITTI depth groundtruth, and eigen split files, then modify the data path in the config file.

Training

python path/to/project/train.py --num-gpus 2 --cfg path/to/config RUN_NAME run_name

Evaluation

python path/to/project/train.py --num-gpus 2 --cfg path/to/config --eval MODEL.WEIGHTS /path/to/checkpoint_file

Results:

KITTI:

Demo:

python tools/demo.py --cfg path/to/config --input path/to/image --output path/to/output_dir MODEL.WEIGHTS /path/to/checkpoint_file

Demo results:

Todo

• add PackNet (I have added it, performance need verification)
• add Dynamic Motion Learning (I have implemented it but still buggy, help welcome!)
• support more datasets

Reference

• detectron2
• monodepth2
• bts
• packnet-sfm