Normal Assisted Stereo Depth Estimation

Uday Kusupati*, Shuo Cheng, Rui Chen and Hao Su, CVPR 2020

* corresponding author

Introduction

Accurate stereo depth estimation plays a critical role in various 3D tasks in both indoor and outdoor environments. Recently, learning-based multi-view stereo methods have demonstrated competitive performance with limited number of views. However, in challenging scenarios, especially when building cross-view correspondences is hard, these methods still cannot produce satisfying results. In this paper, we study how to enforce the consistency between surface normal and depth at training time to improve the performance. We couple the learning of a multi-view normal estimation module and a multi-view depth estimation module. In addition, we propose a novel consistency loss to train an independent consistency module that refines the depths from depth/normal pairs. We find that the joint learning can improve both the prediction of normal and depth, and the accuracy and smoothness can be further improved by enforcing the consistency. Experiments on MVS, SUN3D, RGBD and Scenes11 demonstrate the effectiveness of our method and state-of-the-art performance.

If you find this project useful for your research, please cite:

@InProceedings{Kusupati_2020_CVPR,
author = {Kusupati, Uday and Cheng, Shuo and Chen, Rui and Su, Hao},
title = {Normal Assisted Stereo Depth Estimation},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

How to use

Environment

pytorch/pytorch:1.1.0-cuda10.0-cudnn7.5-runtime

The environment requirements are listed as follows:

• Pytorch 1.0.1
• CUDA 10.0
• CUDNN 7

The following dockers are suggested:

• pytorch/pytorch:1.1.0-cuda10.0-cudnn7.5-runtime
• pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime

Preparation

• Check out the source code

  git clone https://github.com/udaykusupati/NAS.git && cd NAS

• Install dependencies

  pip install pillow scipy==1.2.1 argparse tensorboardX progressbar2 path.py h5py blessings scikit-image

• Prepare Training Data

  • DeMoN datasets

    • Follow data preparation instructions from DPSNet
    • Download normal map data from demon_normals.tar.gz and extract inside dataset directory

    |-- dataset
            |-- new_normals
            |-- preparation
            |-- test
            |-- train
    

  • ScanNet

    • Download scannet.tar.gz
    • Download new_orders.zip and extract in scannet directory

    |-- dataset
    |-- scannet
            |-- new_orders
            |-- test
            |-- test_scene.list
            |-- train
            |-- train_scene.list
            |-- val
            |-- val_scene.list
    

  • SceneFlow datasets

    • Follow dataset download and preparation instructions for "FlyingThings3D", "Driving" and "Monkaa" datasets from GANet and create sceneflow directory.
    • Download sceneflow_normals.tar.gz and extract in sceneflow directory

    |-- dataset
    |-- scannet
    |-- sceneflow
            |-- camera_data
                    |-- TEST
                    |-- TRAIN
            |-- disparity
                    |-- TEST
                    |-- TRAIN
            |-- frames_finalpass
                    |-- TEST
                    |-- TRAIN
            |-- lists
            |-- normals
                    |-- TEST
                    |-- TRAIN
            |-- sceneflow_test.list
            |-- sceneflow_train.list
    

• Download pretrained models in pretrained folder

Testing

• Test with the pretrained model without consistency module

  • DeMoN datasets

    python train.py ./dataset/train --ttype2 test.txt -e -np --pretrained-mvdn pretrained/mvdnet_demon.pth.tar --print-freq 1

  • SUN3D

    python train.py ./dataset/train --ttype2 sun_test.txt -e --pretrained-mvdn pretrained/mvdnet_sun3d.pth.tar --print-freq 1

  • ScanNet

    python train.py ./scannet --dataset scannet --ttype2 test.txt --mindepth 0.25 -e --pretrained-mvdn pretrained/mvdnet_scannet.pth.tar --print-freq 1

  • SceneFlow datasets

    python train_sflow.py ./sceneflow --dataset sceneflow --ttype2 test.txt -e --pretrained-mvdn pretrained/mvdnet_sflow.pth.tar --print-freq 1

• Test with the pretrained model with consistency module

  • SUN3D

    python train.py ./dataset/train --ttype2 sun_test.txt -e --pretrained-mvdn pretrained/mvdnet_sun3d.pth.tar --pretrained-cons pretrained/cons_sun3d.pth.tar -tc --print-freq 1

  • ScanNet

    python train.py ./scannet --dataset scannet --ttype2 test.txt --mindepth 0.25 -e --pretrained-mvdn pretrained/mvdnet_scannet.pth.tar --pretrained-cons pretrained/cons_scannet.pth.tar -tc --print-freq 1

Training

• Training without the consistency module

  • DeMoN datasets

    python train.py ./dataset/train

  • SUN3D

    python train.py ./dataset/train --ttype sun_train.txt --ttype2 sun_val.txt

  • ScanNet

    python train.py ./scannet --dataset scannet --mindepth 0.25

  • SceneFlow datasets

    The primary aim of training on SceneFlow datasets is to compare with GANet, an effective cost aggregation method and we compare it with the cost-aggregation implicitly caused by normal supervision. We set the disparity range to 192 similar to them but we only use 64 levels (sampled uniformly across 192 levels) due to memory constraints. We also set mindepth to 5.45 assuming the focal length to be 1050.0 mm, but for a few scenes with 450.mm it is adjusted appropriately (factor parameter in MVDNet.py)

    python train.py ./sceneflow --dataset sceneflow

• Training with the consistency module

  • SUN3D

    python train.py ./dataset/train --ttype sun_train.txt --ttype2 sun_val.txt -tc

  • ScanNet

    python train.py ./scannet --dataset scannet --mindepth 0.25 -tc

Acknowledgement

The code heavily relies on code from DPSNet (https://github.com/sunghoonim/DPSNet/)