ESTDepth: Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks (CVPR 2021)

Overview

ESTDepth: Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks (CVPR 2021)

Project Page | Video | Paper | Data

We present a novel method for multi-view depth estimation from a single video, which is a critical task in various applications, such as perception, reconstruction and robot navigation. Although previous learning-based methods have demonstrated compelling results, most works estimate depth maps of individual video frames independently, without taking into consideration the strong geometric and temporal coherence among the frames. Moreover, current state-of-the-art (SOTA) models mostly adopt a fully 3D convolution network for cost regularization and therefore require high computational cost, thus limiting their deployment in real-world applications. Our method achieves temporally coherent depth estimation results by using a novel Epipolar Spatio-Temporal (EST) transformer to explicitly associate geometric and temporal correlation with multiple estimated depth maps. Furthermore, to reduce the computational cost, inspired by recent Mixture-of-Experts models, we design a compact hybrid network consisting of a 2D context-aware network and a 3D matching network which learn 2D context information and 3D disparity cues separately.

Here is the official repo for the paper:

Table of contents



Requirements and Installation

This code is implemented in PyTorch.

The code has been tested on the following system:

To install, first clone this repo and install all dependencies:

conda env create -f environment.yml

Option: install apex to enable synchronized batch normalization

git clone https://github.com/NVIDIA/apex.git
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Dataset

Please also cite the original papers if you use any of them in your work.

Dataset Notes on Dataset Split
ScanNet see ./data/scannet_split/
7scenes see ./data/7scenes/test.txt

Train a new model

In the training stage, our model takes a sequence of 5 frames as input, with a batch size of 4 sequences on 4 GPUs. We use the following code to train a model:

python -m torch.distributed.launch --nproc_per_node=4 train_hybrid.py  --using_apex  --sync_bn \
--datapath /userhome/35/xxlong/dataset/scannet_whole/  \
--testdatapath /userhome/35/xxlong/dataset/scannet_test/ \
--reloadscan True \
--batch_size 1 --seq_len 5 --mode train --summary_freq 10 \
--epochs 7 --lr 0.00004 --lrepochs 2,4,6,8:2 \
--logdir ./logs/hybrid_res50_ndepths64 \
--resnet 50 --ndepths 64 --IF_EST_transformer False \
--depth_min 0.1 --depth_max 10. |  tee -a ./logs/hybrid_res50_ndepths64/log.txt
bash train_hybrid.sh

Evaluation

Once the model is trained, the following command is used to evaluate test images given the trained_model.

Our model has two testing modes: Joint and ESTM

For Joint mode, run:

bash eval_hybrid.sh

For ESTM mode, run:

bash eval_hybrid_seq.sh

License

ESTDepth is MIT-licensed. The license applies to the pre-trained models as well.

Citation

Please cite as

@InProceedings{Long_2021_CVPR,
    author    = {Long, Xiaoxiao and Liu, Lingjie and Li, Wei and Theobalt, Christian and Wang, Wenping},
    title     = {Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8258-8267}
Owner
Xiaoxiao Long Ph.D. student in HKU
A facial recognition doorbell system using a Raspberry Pi

Facial Recognition Doorbell This project expands on the person-detecting doorbell system to allow it to identify faces, and announce names accordingly

rydercalmdown 22 Apr 15, 2022
Diverse Image Captioning with Context-Object Split Latent Spaces (NeurIPS 2020)

Diverse Image Captioning with Context-Object Split Latent Spaces This repository is the PyTorch implementation of the paper: Diverse Image Captioning

Visual Inference Lab @TU Darmstadt 34 Nov 21, 2022
Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The original code is written in keras.

CasRel-pytorch-reimplement Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The o

longlongman 170 Dec 01, 2022
Official implementation of ETH-XGaze dataset baseline

ETH-XGaze baseline Official implementation of ETH-XGaze dataset baseline. ETH-XGaze dataset ETH-XGaze dataset is a gaze estimation dataset consisting

Xucong Zhang 134 Jan 03, 2023
This repository implements variational graph auto encoder by Thomas Kipf.

Variational Graph Auto-encoder in Pytorch This repository implements variational graph auto-encoder by Thomas Kipf. For details of the model, refer to

DaehanKim 215 Jan 02, 2023
ADGAN - The Implementation of paper Controllable Person Image Synthesis with Attribute-Decomposed GAN

ADGAN - The Implementation of paper Controllable Person Image Synthesis with Attribute-Decomposed GAN CVPR 2020 (Oral); Pose and Appearance Attributes Transfer;

Men Yifang 400 Dec 29, 2022
GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)

GemNet: Universal Directional Graph Neural Networks for Molecules Reference implementation in PyTorch of the geometric message passing neural network

Data Analytics and Machine Learning Group 124 Dec 30, 2022
Code implementation from my Medium blog post: [Transformers from Scratch in PyTorch]

transformer-from-scratch Code for my Medium blog post: Transformers from Scratch in PyTorch Note: This Transformer code does not include masked attent

Frank Odom 27 Dec 21, 2022
Image-to-Image Translation in PyTorch

CycleGAN and pix2pix in PyTorch New: Please check out contrastive-unpaired-translation (CUT), our new unpaired image-to-image translation model that e

Jun-Yan Zhu 19k Jan 07, 2023
kullanışlı ve işinizi kolaylaştıracak bir araç

Hey merhaba! işte çok sorulan sorularının cevabı ve sorunlarının çözümü; Soru= İçinde var denilen birçok şeyi göremiyorum bunun sebebi nedir? Cevap= B

Sexettin 16 Dec 17, 2022
StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion Yinghao Aaron Li, Ali Zare, Nima Mesgarani We pres

Aaron (Yinghao) Li 282 Jan 01, 2023
Tensorflow implementation of Character-Aware Neural Language Models.

Character-Aware Neural Language Models Tensorflow implementation of Character-Aware Neural Language Models. The original code of author can be found h

Taehoon Kim 751 Dec 26, 2022
Visualizer using audio and semantic analysis to explore BigGAN (Brock et al., 2018) latent space.

BigGAN Audio Visualizer Description This visualizer explores BigGAN (Brock et al., 2018) latent space by using pitch/tempo of an audio file to generat

Rush Kapoor 2 Nov 21, 2022
[NeurIPS 2021] "Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks" by Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang, David Cox, Yingyan Lin

Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks Yonggan Fu, Qixuan Yu, Yang Zhang, S

12 Dec 11, 2022
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection

RODD Official Implementation of 2022 CVPRW Paper RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection Introduction: Recent studie

Umar Khalid 17 Oct 11, 2022
Zalo AI challenge 2021 task hum to song

Zalo AI challenge 2021 task Hum to Song pipeline: Chuẩn bị dữ liệu cho quá trình train: Sửa các file đường dẫn trong config/preprocess.yaml raw_path:

Vo Van Phuc 105 Dec 16, 2022
Pytorch implementation for Patient Knowledge Distillation for BERT Model Compression

Patient Knowledge Distillation for BERT Model Compression Knowledge distillation for BERT model Installation Run command below to install the environm

Siqi 180 Dec 19, 2022
Source code for the plant extraction workflow introduced in the paper “Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision”

Plant extraction workflow Source code for the plant extraction workflow introduced in the paper "Agricultural Plant Cataloging and Establishment of a

Maurice Günder 0 Apr 22, 2022
PyTorch implementation of paper A Fast Knowledge Distillation Framework for Visual Recognition.

FKD: A Fast Knowledge Distillation Framework for Visual Recognition Official PyTorch implementation of paper A Fast Knowledge Distillation Framework f

Zhiqiang Shen 129 Dec 24, 2022
Code for the CVPR2021 workshop paper "Noise Conditional Flow Model for Learning the Super-Resolution Space"

NCSR: Noise Conditional Flow Model for Learning the Super-Resolution Space Official NCSR training PyTorch Code for the CVPR2021 workshop paper "Noise

57 Oct 03, 2022