[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 πŸ”₯

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 πŸ’₯

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:

...
└── Votenet (or GroupFree3D)
    β”œβ”€β”€ ...
    └── scannet
        β”œβ”€β”€ ...
        β”œβ”€β”€ scannet_train_detection_data_md40
        β”œβ”€β”€ scannet_train_detection_data_md40_obj_aug
        └── 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:

...
└── Votenet
    β”œβ”€β”€ ...
    └── matterport
        β”œβ”€β”€ ...
        β”œβ”€β”€ matterport_train_detection_data_md40
        β”œβ”€β”€ matterport_train_detection_data_md40_obj_aug
        └── 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
Image-Scaling Attacks and Defenses

Image-Scaling Attacks & Defenses This repository belongs to our publication: Erwin Quiring, David Klein, Daniel Arp, Martin Johns and Konrad Rieck. Ad

Erwin Quiring 163 Nov 21, 2022
PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

About PyTorch 1.2.0 Now the master branch supports PyTorch 1.2.0 by default. Due to the serious version problem (especially torch.utils.data.dataloade

Sanghyun Son 2.1k Jan 01, 2023
A collection of scripts I developed for personal and working projects.

A collection of scripts I developed for personal and working projects Table of contents Introduction Repository diagram structure List of scripts pyth

Gianluca Bianco 109 Dec 26, 2022
C3D is a modified version of BVLC caffe to support 3D ConvNets.

C3D C3D is a modified version of BVLC caffe to support 3D convolution and pooling. The main supporting features include: Training or fine-tuning 3D Co

Meta Archive 1.1k Nov 14, 2022
A High-Performance Distributed Library for Large-Scale Bundle Adjustment

MegBA: A High-Performance and Distributed Library for Large-Scale Bundle Adjustment This repo contains an official implementation of MegBA. MegBA is a

旷视研穢陒 3D η»„ 336 Dec 27, 2022
[ECCV 2020] XingGAN for Person Image Generation

Contents XingGAN or CrossingGAN Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Evaluation Acknowl

Hao Tang 218 Oct 29, 2022
CNN designed for pansharpening

PROGRESSIVE BAND-SEPARATED CONVOLUTIONAL NEURAL NETWORK FOR MULTISPECTRAL PANSHARPENING This repository contains main code for the paper PROGRESSIVE B

SerendipitysX 3 Dec 29, 2021
[CVPR 2022] PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision (Oral)

PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision Kehong Gong*, Bingbing Li*, Jianfeng Zhang*, Ta

256 Dec 28, 2022
Improving Deep Network Debuggability via Sparse Decision Layers

Improving Deep Network Debuggability via Sparse Decision Layers This repository contains the code for our paper: Leveraging Sparse Linear Layers for D

Madry Lab 35 Nov 14, 2022
HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement

HiFi++ : a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement This is the unofficial implementation of Vocoder part of

Rishikesh (ΰ€‹ΰ€·ΰ€Ώΰ€•ΰ₯‡ΰ€Ά) 118 Dec 29, 2022
Multi Task Vision and Language

12-in-1: Multi-Task Vision and Language Representation Learning Please cite the following if you use this code. Code and pre-trained models for 12-in-

Facebook Research 712 Dec 19, 2022
A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

Adversarial Video Generation This project implements a generative adversarial network to predict future frames of video, as detailed in "Deep Multi-Sc

Matt Cooper 704 Nov 26, 2022
PyTorch implementations of the paper: "DR.VIC: Decomposition and Reasoning for Video Individual Counting, CVPR, 2022"

DRNet for Video Indvidual Counting (CVPR 2022) Introduction This is the official PyTorch implementation of paper: DR.VIC: Decomposition and Reasoning

tao han 35 Nov 22, 2022
RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving (AAAI2021). RTS3D is efficiency and accuracy s

71 Nov 29, 2022
You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors In this paper, we propose a novel local descriptor-based fra

Haiping Wang 80 Dec 15, 2022
Parallel Latent Tree-Induction for Faster Sequence Encoding

FastTrees This repository contains the experimental code supporting the FastTrees paper by Bill Pung. Software Requirements Python 3.6, NLTK and PyTor

Bill Pung 4 Mar 29, 2022
CVPR2020 Counterfactual Samples Synthesizing for Robust VQA

CVPR2020 Counterfactual Samples Synthesizing for Robust VQA This repo contains code for our paper "Counterfactual Samples Synthesizing for Robust Visu

72 Dec 22, 2022
Pytorch implementation for "Open Compound Domain Adaptation" (CVPR 2020 ORAL)

Open Compound Domain Adaptation [Project] [Paper] [Demo] [Blog] Overview Open Compound Domain Adaptation (OCDA) is the author's re-implementation of t

Zhongqi Miao 137 Dec 15, 2022
Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics

[AAAI2022] Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics Overall pipeline of OCN. Paper Link: [arXiv] [AAAI

13 Nov 21, 2022
fklearn: Functional Machine Learning

fklearn: Functional Machine Learning fklearn uses functional programming principles to make it easier to solve real problems with Machine Learning. Th

nubank 1.4k Dec 07, 2022