[CVPR 2022] Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement

Overview

Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement

Announcement ๐Ÿ”ฅ

We have not tested the code yet. We will finish this project by April.

Introduction

This repo contains PyTorch implementation for paper Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement (CVPR2022)

overview

@inproceedings{xu2022br,
author = {Xu, Xiuwei and Wang, Yifan and Zheng, Yu and Rao, Yongming and Lu, Jiwen and Zhou, Jie},
title = {Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022}
}

Other papers related to 3D object detection with synthetic shape:

  • RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection (ICCV 2021)

New dataset ๐Ÿ’ฅ

We conduct additional experiment on the more challenging Matterport3D dataset. From ModelNet40 and Matterport3D, we select all 13 shared categories, each containing more than 80 object instances in Matterport3D training set, to construct our benchmark (Matterport3d-md40). Below is the performance of FSB, WSB and BR (point-version) based on Votenet: overview

Note that we use OpenCV to estimate the rotated bounding boxes (RBB) as ground-truth, instead of the axis-aligned bounding boxes used in ScanNet-md40 benchmark.

ScanNet-md40 and Matterport3d-md40 are two more challenging benckmarks for indoor 3D object detection. We hope they will promote future research on small object detection and synthetic-to-real scene understanding.

Dependencies

We evaluate this code with Pytorch 1.8.1 (cuda11), which is based on the official implementation of Votenet and GroupFree3D. Please follow the requirements of them to prepare the environment. Other packages can be installed using:

pip install open3d sklearn tqdm

Current code base is tested under following environment:

  1. Python 3.6.13
  2. PyTorch 1.8.1
  3. numpy 1.19.2
  4. open3d 0.12.0
  5. opencv-python 4.5.1.48
  6. plyfile 0.7.3
  7. scikit-learn 0.24.1

Data preparation

ScanNet

To start from the raw data, you should:

  • Follow the README under GroupFree3D/scannet or Votenet/scannet to generate the real scenes.
  • Follow the README under ./data_generation/ScanNet to generate the virtual scenes.

The processed data can also be downloaded from here. They should be placed to paths:

./detection/Votenet/scannet/
./detection/GroupFree3D/scannet/

After that, the file directory should be like:

...
โ””โ”€โ”€ Votenet (or GroupFree3D)
    โ”œโ”€โ”€ ...
    โ””โ”€โ”€ scannet
        โ”œโ”€โ”€ ...
        โ”œโ”€โ”€ scannet_train_detection_data_md40
        โ”œโ”€โ”€ scannet_train_detection_data_md40_obj_aug
        โ””โ”€โ”€ scannet_train_detection_data_md40_obj_mesh_aug

Matterport3D

To start from the raw data, you should:

  • Follow the README under Votenet/scannet to generate the real scenes.
  • Follow the README under ./data_generation/Matterport3D to generate the virtual scenes.

The processed data can also be downloaded from here.

The file directory should be like:

...
โ””โ”€โ”€ Votenet
    โ”œโ”€โ”€ ...
    โ””โ”€โ”€ matterport
        โ”œโ”€โ”€ ...
        โ”œโ”€โ”€ matterport_train_detection_data_md40
        โ”œโ”€โ”€ matterport_train_detection_data_md40_obj_aug
        โ””โ”€โ”€ matterport_train_detection_data_md40_obj_mesh_aug

Usage

Please follow the instructions below to train different models on ScanNet. Change --dataset scannet to --dataset matterport for training on Matterport3D.

Votenet

1. Fully-Supervised Baseline

To train the Fully-Supervised Baseline (FSB) on Scannet data:

# Recommended GPU num: 1

cd Votenet

CUDA_VISIBLE_DEVICES=0 python train_Votenet_FSB.py --dataset scannet --log_dir log_Votenet_FSB --num_point 40000

2. Weakly-Supervised Baseline

To train the Weakly-Supervised Baseline (WSB) on Scannet data:

# Recommended num of GPUs: 1

CUDA_VISIBLE_DEVICES=0 python train_Votenet_WSB.py --dataset scannet --log_dir log_Votenet_WSB --num_point 40000

3. Back To Reality

To train BR (mesh-version) on Scannet data, please run:

# Recommended num of GPUs: 2

CUDA_VISIBLE_DEVICES=0,1 python train_Votenet_BR.py --dataset scannet --log_dir log_Votenet_BRM --num_point 40000

CUDA_VISIBLE_DEVICES=0,1 python train_Votenet_BR_CenterRefine --dataset scannet --log_dir log_Votenet_BRM_Refine --num_point 40000 --checkpoint_path log_Votenet_BRM/train_BR.tar

To train BR (point-version) on Scannet data, please run:

# Recommended num of GPUs: 2

CUDA_VISIBLE_DEVICES=0,1 python train_Votenet_BR.py --dataset scannet --log_dir log_Votenet_BRP --num_point 40000 --dataset_without_mesh

CUDA_VISIBLE_DEVICES=0,1 python train_Votenet_BR_CenterRefine --dataset scannet --log_dir log_Votenet_BRP_Refine --num_point 40000 --checkpoint_path log_Votenet_BRP/train_BR.tar --dataset_without_mesh

GroupFree3D

1. Fully-Supervised Baseline

To train the Fully-Supervised Baseline (FSB) on Scannet data:

# Recommended num of GPUs: 4

cd GroupFree3D

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_FSB.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_FSB --batch_size 4

2. Weakly-Supervised Baseline

To train the Weakly-Supervised Baseline (WSB) on Scannet data:

# Recommended num of GPUs: 4

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_WSB.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_WSB --batch_size 4

3. Back To Reality

To train BR (mesh-version) on Scannet data, please run:

# Recommended num of GPUs: 4

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_BR.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_BRM --batch_size 4

# Recommended num of GPUs: 6

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_BR_CenterRefine.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.001 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_BRM_Refine --checkpoint_path <[checkpoint_path_of_groupfree3D]/ckpt_epoch_last.pth> --max_epoch 120 --val_freq 10 --save_freq 20 --batch_size 2

To train BR (point-version) on Scannet data, please run:

# Recommended num of GPUs: 4

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_BR.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_BRP --batch_size 4 --dataset_without_mesh

# Recommended num of GPUs: 6

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_BR_CenterRefine.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.001 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_BRP_Refine --checkpoint_path <[checkpoint_path_of_groupfree3D]/ckpt_epoch_last.pth> --max_epoch 120 --val_freq 10 --save_freq 20 --batch_size 2 --dataset_without_mesh

TODO list

We will add the following to this repo:

  • Virtual scene generation for Matterport3D
  • Data and code for training Votenet (both baseline and BR) on the Matterport3D dataset

Acknowledgements

We thank a lot for the flexible codebase of Votenet and GroupFree3D.

Owner
Xiuwei Xu
3D vision, data/computation-efficient learning
Xiuwei Xu
I3-master-layout - Simple master and stack layout script

Simple master and stack layout script | ------ | ----- | | | | | Ma

Tobias S 18 Dec 05, 2022
gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions

gtfs2vec This is a companion repository for a gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions publication. Vis

Politechnika Wrocล‚awska - repozytorium dla informatykรณw 5 Oct 10, 2022
๐Ÿ”ฅ Cogitare - A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python

Cogitare is a Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python. A friendly interface for beginners and a powerful too

Cogitare - Modern and Easy Deep Learning with Python 76 Sep 30, 2022
Global Rhythm Style Transfer Without Text Transcriptions

Global Prosody Style Transfer Without Text Transcriptions This repository provides a PyTorch implementation of AutoPST, which enables unsupervised glo

Kaizhi Qian 193 Dec 30, 2022
NeRF Meta-Learning with PyTorch

NeRF Meta Learning With PyTorch nerf-meta is a PyTorch re-implementation of NeRF experiments from the paper "Learned Initializations for Optimizing Co

Sanowar Raihan 78 Dec 18, 2022
source code for 'Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge' by A. Shah, K. Shanmugam, K. Ahuja

Source code for "Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge" Reference: Abhin Shah, Karthikeyan Shanmugam, Kartik Ahu

Abhin Shah 1 Jun 03, 2022
PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

Unsupervised_IEPGAN This is the PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer. Ha

25 Oct 26, 2022
๐Ÿงฎ Matrix Factorization for Collaborative Filtering is just Solving an Adjoint Latent Dirichlet Allocation Model after All

Accompanying source code to the paper "Matrix Factorization for Collaborative Filtering is just Solving an Adjoint Latent Dirichlet Allocation Model A

Florian Wilhelm 39 Dec 03, 2022
A program to recognize fruits on pictures or videos using yolov5

Yolov5 Fruits Detector Requirements Either Linux or Windows. We recommend Linux for better performance. Python 3.6+ and PyTorch 1.7+. Installation To

Fateme Zamanian 30 Jan 06, 2023
The fastest way to visualize GradCAM with your Keras models.

VizGradCAM VizGradCam is the fastest way to visualize GradCAM in Keras models. GradCAM helps with providing visual explainability of trained models an

58 Nov 19, 2022
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight)

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight) Abstract Due to the limited and even imbalanced dat

Hanzhe Hu 99 Dec 12, 2022
ไธ€ไธช่ฟ่กŒๅœจ ๐ž๐ฅ๐ž๐œ๐•๐Ÿ๐ ๆˆ– ๐ช๐ข๐ง๐ ๐ฅ๐จ๐ง๐  ็ญ‰ๅฎšๆ—ถ้ขๆฟ็š„็ญพๅˆฐ้กน็›ฎ

ๅฎšๆ—ถ้ขๆฟไธŠ็š„็ญพๅˆฐ็›’ ไธ€ไธช่ฟ่กŒๅœจ ๐ž๐ฅ๐ž๐œ๐•๐Ÿ๐ ๆˆ– ๐ช๐ข๐ง๐ ๐ฅ๐จ๐ง๐  ็ญ‰ๅฎšๆ—ถ้ขๆฟ็š„็ญพๅˆฐ้กน็›ฎ ๐ž๐ฅ๐ž๐œ๐•๐Ÿ๐ ๐ช๐ข๐ง๐ ๐ฅ๐จ๐ง๐  ็‰นๅˆซๅฃฐๆ˜Ž ๆœฌไป“ๅบ“ๅ‘ๅธƒ็š„่„šๆœฌๅŠๅ…ถไธญๆถ‰ๅŠ็š„ไปปไฝ•่งฃ้”ๅ’Œ่งฃๅฏ†ๅˆ†ๆž่„šๆœฌ๏ผŒไป…็”จไบŽๆต‹่ฏ•ๅ’Œๅญฆไน ็ ”็ฉถ๏ผŒ็ฆๆญข็”จไบŽๅ•†ไธš็”จ้€”๏ผŒไธ่ƒฝไฟ่ฏๅ…ถๅˆ

Leon 1.1k Dec 30, 2022
Controlling the MicriSpotAI robot from scratch

Abstract: The SpotMicroAI project is designed to be a low cost, easily built quadruped robot. The design is roughly based off of Boston Dynamics quadr

Florian Wilk 405 Jan 05, 2023
OpenLT: An open-source project for long-tail classification

OpenLT: An open-source project for long-tail classification Supported Methods for Long-tailed Recognition: Cross-Entropy Loss Focal Loss (ICCV'17) Cla

Ming Li 37 Sep 15, 2022
Face-Recognition-Attendence-System - This face recognition Attendence system using Python

Face-Recognition-Attendence-System I have developed this face recognition Attend

Riya Gupta 4 May 10, 2022
IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling

IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling This is my code, data and approach for the IEEE-CIS Technical Challen

3 Sep 18, 2022
Answering Open-Domain Questions of Varying Reasoning Steps from Text

This repository contains the authors' implementation of the Iterative Retriever, Reader, and Reranker (IRRR) model in the EMNLP 2021 paper "Answering Open-Domain Questions of Varying Reasoning Steps

26 Dec 22, 2022
Extracting and filtering paraphrases by bridging natural language inference and paraphrasing

nli2paraphrases Source code repository accompanying the preprint Extracting and filtering paraphrases by bridging natural language inference and parap

Matej Klemen 1 Mar 09, 2022
Malware Bypass Research using Reinforcement Learning

Malware Bypass Research using Reinforcement Learning

Bobby Filar 76 Dec 26, 2022
AbelNN: Deep Learning Python module from scratch

AbelNN: Deep Learning Python module from scratch I have implemented several neural networks from scratch using only Numpy. I have designed the module

Abel 2 Apr 12, 2022