marenan / bi3d Goto Github PK

Bi3D: Stereo Depth Estimation via Binary Classifications
Abhishek Badki, Alejandro Troccoli, Kihwan Kim, Jan Kautz, Pradeep Sen, and Orazio Gallo
IEEE CVPR 2020

Stereo-based depth estimation is a cornerstone of computer vision, with state-of-the-art methods delivering accurate results in real time. For several applications such as autonomous navigation, however, it may be useful to trade accuracy for lower latency. We present Bi3D, a method that estimates depth via a series of binary classifications. Rather than testing if objects are at a particular depth D, as existing stereo methods do, it classifies them as being closer or farther than D. This property offers a powerful mechanism to balance accuracy and latency. Given a strict time budget, Bi3D can detect objects closer than a given distance in as little as a few milliseconds, or estimate depth with arbitrarily coarse quantization, with complexity linear with the number of quantization levels. Bi3D can also use the allotted quantization levels to get continuous depth, but in a specific depth range. For standard stereo (i.e., continuous depth on the whole range), our method is close to or on par with state-of-the-art, finely tuned stereo methods.

https://arxiv.org/pdf/2005.07274.pdf

@InProceedings{badki2020Bi3D,
author = {Badki, Abhishek and Troccoli, Alejandro and Kim, Kihwan and Kautz, Jan and Sen, Pradeep and Gallo, Orazio},
title = {{Bi3D}: {S}tereo Depth Estimation via Binary Classifications},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2020}
}

or the arXiv paper

@InProceedings{badki2020Bi3D,
author = {Badki, Abhishek and Troccoli, Alejandro and Kim, Kihwan and Kautz, Jan and Sen, Pradeep and Gallo, Orazio},
title = {{Bi3D}: {S}tereo Depth Estimation via Binary Classifications},
booktitle = {arXiv preprint arXiv:2005.07274},
year = {2020}
}

Copyright (C) 2020 NVIDIA Corporation. All rights reserved.

Licensed under the NVIDIA Source Code License

We offer two ways of setting up your environemnt, through Docker or Conda.

For convenience, we provide a Dockerfile to build a container image to run the code. The image will contain the Python dependencies.

System requirements:

1. Docker (Tested on version 19.03.11)

2. NVIDIA Docker

3. NVIDIA GPU driver.

Build the container image:

docker build -t bi3d . -f envs/bi3d_pytorch_19_01.DockerFile

To launch the container, run the following:

docker run --rm -it --gpus=all -v $(pwd):/bi3d -w /bi3d --net=host --ipc=host bi3d:latest /bin/bash

All dependencies will be installed automatically using the following:

conda env create -f envs/bi3d_conda_env.yml 

You can activate the environment by running:

conda activate bi3d

Download the pre-trained models here.

cd src
# RUN DEMO FOR SCENEFLOW DATASET 
sh run_demo_sf.sh
# RUN DEMO FOR KITTI15 DATASET
sh run_demo_kitti15.sh