Deep Two-View Structure-from-Motion Revisited

Overview

Deep Two-View Structure-from-Motion Revisited

This repository provides the code for our CVPR 2021 paper Deep Two-View Structure-from-Motion Revisited.

We have provided the functions for training, validating, and visualization.

Note: some config flags are designed for ablation study, and we have a plan to re-org the codes later. Please feel free to submit issues if you feel confused about some parts.

Requirements

Python = 3.6.x
Pytorch >= 1.6.0
CUDA >= 10.1

and the others could be installed by

pip install -r requirements.txt

Pytorch from 1.1.0 to 1.6.0 should also work well, but it will disenable mixed precision training, and we have not tested it.

To use the RANSAC five-point algorithm, you also need to

cd RANSAC_FiveP

python setup.py install --user

The CUDA extension would be installed as 'essential_matrix'. Tested under Ubuntu and CUDA 10.1.

Models

Pretrained models are provided here.

KITTI Depth

To reproduce our results, please first download the KITTI dataset RAW data and 14GB official depth maps. You should also download the split files provided by us, and unzip them into the root of the KITTI raw data. Then, modify the gt_depth_dir (KITTI_loader.py, L278) to the address of KITTI official depth maps.

For training,

python main.py -b 32 --lr 0.0005 --nlabel 128 --fix_flownet \
--data PATH/TO/YOUR/KITTI/DATASET --cfg cfgs/kitti.yml \
--pretrained-depth depth_init.pth.tar --pretrained-flow flow_init.pth.tar

For evaluation,

python main.py -v -b 1 -p 1 --nlabel 128 \
--data PATH/TO/YOUR/KITTI/DATASET --cfg cfgs/kitti.yml \
--pretrained kitti.pth.tar"

The default evaluation split is Eigen, where the metric abs_rel should be around 0.053 and rmse should be close to 2.22. If you would like to use the Eigen SfM split, please set cfg.EIGEN_SFM = True and cfg.KITTI_697 = False.

KITTI Pose

For fair comparison, we use a KITTI odometry evaluation toolbox as provided here. Please generate poses by sequence, and evaluate the results correspondingly.

Acknowledgment:

Thanks Shihao Jiang and Dylan Campbell for sharing the implementation of the GPU-accelerated RANSAC Five-point algorithm. We really appreciate the valuable feedback from our area chairs and reviewers. We would like to thank Charles Loop for helpful discussions and Ke Chen for providing field test images from NVIDIA AV cars.

BibTex:

@article{wang2021deep,
  title={Deep Two-View Structure-from-Motion Revisited},
  author={Wang, Jianyuan and Zhong, Yiran and Dai, Yuchao and Birchfield, Stan and Zhang, Kaihao and Smolyanskiy, Nikolai and Li, Hongdong},
  journal={CVPR},
  year={2021}
}
Owner
Jianyuan Wang
Computer Vision
Jianyuan Wang
DIR-GNN - Discovering Invariant Rationales for Graph Neural Networks

DIR-GNN "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

Ying-Xin (Shirley) Wu 70 Nov 13, 2022
Jittor Medical Segmentation Lib -- The assignment of Pattern Recognition course (2021 Spring) in Tsinghua University

THU模式识别2021春 -- Jittor 医学图像分割 模型列表 本仓库收录了课程作业中同学们采用jittor框架实现的如下模型: UNet SegNet DeepLab V2 DANet EANet HarDNet及其改动HarDNet_alter PSPNet OCNet OCRNet DL

48 Dec 26, 2022
yolox_backbone is a deep-learning library and is a collection of YOLOX Backbone models.

YOLOX-Backbone yolox-backbone is a deep-learning library and is a collection of YOLOX backbone models. Install pip install yolox-backbone Load a Pret

Yonghye Kwon 21 Dec 28, 2022
Code for training and evaluation of the model from "Language Generation with Recurrent Generative Adversarial Networks without Pre-training"

Language Generation with Recurrent Generative Adversarial Networks without Pre-training Code for training and evaluation of the model from "Language G

Amir Bar 253 Sep 14, 2022
PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, wav2lip, picture repair, image editing, photo2cartoon, image style transfer, and so on.

English | 简体中文 PaddleGAN PaddleGAN provides developers with high-performance implementation of classic and SOTA Generative Adversarial Networks, and s

6.4k Jan 09, 2023
PyTorch implementations of the paper: "Learning Independent Instance Maps for Crowd Localization"

IIM - Crowd Localization This repo is the official implementation of paper: Learning Independent Instance Maps for Crowd Localization. The code is dev

tao han 91 Nov 10, 2022
ExCon: Explanation-driven Supervised Contrastive Learning

ExCon: Explanation-driven Supervised Contrastive Learning Contributors of this repo: Zhibo Zhang ( Zhibo (Darren) Zhang 18 Nov 01, 2022

Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies

An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks Novel and high-performance medical ima

14 Dec 18, 2022
Image transformations designed for Scene Text Recognition (STR) data augmentation. Published at ICCV 2021 Workshop on Interactive Labeling and Data Augmentation for Vision.

Data Augmentation for Scene Text Recognition (ICCV 2021 Workshop) (Pronounced as "strog") Paper Arxiv Why it matters? Scene Text Recognition (STR) req

Rowel Atienza 152 Dec 28, 2022
K-FACE Analysis Project on Pytorch

Installation Setup with Conda # create a new environment conda create --name insightKface python=3.7 # or over conda activate insightKface #install t

Jung Jun Uk 7 Nov 10, 2022
Learning Time-Critical Responses for Interactive Character Control

Learning Time-Critical Responses for Interactive Character Control Abstract This code implements the paper Learning Time-Critical Responses for Intera

Movement Research Lab 227 Dec 31, 2022
A 3D Dense mapping backend library of SLAM based on taichi-Lang designed for the aerial swarm.

TaichiSLAM This project is a 3D Dense mapping backend library of SLAM based Taichi-Lang, designed for the aerial swarm. Intro Taichi is an efficient d

XuHao 230 Dec 19, 2022
Multistream CNN for Robust Acoustic Modeling

Multistream Convolutional Neural Network (CNN) A multistream CNN is a novel neural network architecture for robust acoustic modeling in speech recogni

ASAPP Research 37 Sep 21, 2022
Automatic tool focused on deriving metallicities of open clusters

metalcode Automatic tool focused on deriving metallicities of open clusters. Based on the method described in Pöhnl & Paunzen (2010, https://ui.adsabs

2 Dec 13, 2021
Tensorflow 2 implementation of the paper: Learning and Evaluating Representations for Deep One-class Classification published at ICLR 2021

Deep Representation One-class Classification (DROC). This is not an officially supported Google product. Tensorflow 2 implementation of the paper: Lea

Google Research 137 Dec 23, 2022
MARE - Multi-Attribute Relation Extraction

MARE - Multi-Attribute Relation Extraction Repository for the paper submission: #TODO: insert link, when available Environment Tested with Ubuntu 18.0

0 May 11, 2021
Generative Art Using Neural Visual Grammars and Dual Encoders

Generative Art Using Neural Visual Grammars and Dual Encoders Arnheim 1 The original algorithm from the paper Generative Art Using Neural Visual Gramm

DeepMind 231 Jan 05, 2023
DLWP: Deep Learning Weather Prediction

DLWP: Deep Learning Weather Prediction DLWP is a Python project containing data-

Kushal Shingote 3 Aug 14, 2022
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations

Transfer-Learning-in-Reinforcement-Learning Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations Final Report Tra

Trung Hieu Tran 4 Oct 17, 2022
Code for our paper Aspect Sentiment Quad Prediction as Paraphrase Generation in EMNLP 2021.

Aspect Sentiment Quad Prediction (ASQP) This repo contains the annotated data and code for our paper Aspect Sentiment Quad Prediction as Paraphrase Ge

Isaac 39 Dec 11, 2022