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

Related tags

Deep Learningobject-detectionweakly-supervised-learning3d-vison
Overview

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

Announcement 🔥

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 💥

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)
    ├── ...
    └── scannet
        ├── ...
        ├── scannet_train_detection_data_md40
        ├── 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
    ├── ...
    └── matterport
        ├── ...
        ├── matterport_train_detection_data_md40
        ├── 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
GitHub Repository
This is a template for the Non-autoregressive Deep Learning-Based TTS model (in PyTorch).

Non-autoregressive Deep Learning-Based TTS Template This is a template for the Non-autoregressive TTS model. It contains Data Preprocessing Pipeline D

Keon Lee 13 Dec 05, 2022
The official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness.

This repository is the official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness. Requirements pip install -r requi

Jie Ren 17 Dec 12, 2022
Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

snc4onnx Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools 1.

Katsuya Hyodo 8 Oct 13, 2022
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
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach This is the implementation of traffic prediction code in DTMP based on PyTo

chenxin 1 Dec 19, 2021
Exploration of some patients clinical variables.

Answer_ALS_clinical_data Exploration of some patients clinical variables. All the clinical / metadata data is available here: https://data.answerals.o

1 Jan 20, 2022
DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction This is the implementation of DeepSTD in

5 Sep 26, 2022
Repository for paper "Non-intrusive speech intelligibility prediction from discrete latent representations"

Non-Intrusive Speech Intelligibility Prediction from Discrete Latent Representations Official repository for paper "Non-Intrusive Speech Intelligibili

Alex McKinney 5 Oct 25, 2022
Autonomous racing with the Anki Overdrive

Anki Autonomous Racing Autonomous racing with the Anki Overdrive. Using the Overdrive-Python API (https://github.com/xerodotc/overdrive-python) develo

3 Dec 11, 2022
The code for SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network.

SAG-DTA The code is the implementation for the paper 'SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network'. Requirements py

Shugang Zhang 7 Aug 02, 2022
Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

PackNet: https://arxiv.org/abs/1711.05769 Pretrained models are available here: https://uofi.box.com/s/zap2p03tnst9dfisad4u0sfupc0y1fxt Datasets in Py

Arun Mallya 216 Jan 05, 2023
[ICML'21] Estimate the accuracy of the classifier in various environments through self-supervision

What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments? [Paper] [ICML'21 Project] PyTorch Implementation T

24 Oct 26, 2022
Bayesian optimisation library developped by Huawei Noah's Ark Library

Bayesian Optimisation Research This directory contains official implementations for Bayesian optimisation works developped by Huawei R&D, Noah's Ark L

HUAWEI Noah's Ark Lab 395 Dec 30, 2022
[CVPR'21] DeepSurfels: Learning Online Appearance Fusion

DeepSurfels: Learning Online Appearance Fusion Paper | Video | Project Page This is the official implementation of the CVPR 2021 submission DeepSurfel

Online Reconstruction 52 Nov 14, 2022
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

Monitor deep learning model training and hardware usage from mobile. 🔥 Features Monitor running experiments from mobile phone (or laptop) Monitor har

labml.ai 1.2k Dec 25, 2022
Ascend your Jupyter Notebook usage

Jupyter Ascending Sync Jupyter Notebooks from any editor About Jupyter Ascending lets you edit Jupyter notebooks from your favorite editor, then insta

Untitled AI 254 Jan 08, 2023
Pixel Consensus Voting for Panoptic Segmentation (CVPR 2020)

Implementation for Pixel Consensus Voting (CVPR 2020). This codebase contains the essential ingredients of PCV, including various spatial discretizati

Haochen 23 Oct 25, 2022
Self-Supervised Image Denoising via Iterative Data Refinement

Self-Supervised Image Denoising via Iterative Data Refinement Yi Zhang1, Dasong Li1, Ka Lung Law2, Xiaogang Wang1, Hongwei Qin2, Hongsheng Li1 1CUHK-S

Zhang Yi 72 Jan 01, 2023
SOLO and SOLOv2 for instance segmentation, ECCV 2020 & NeurIPS 2020.

SOLO: Segmenting Objects by Locations This project hosts the code for implementing the SOLO algorithms for instance segmentation. SOLO: Segmenting Obj

Xinlong Wang 1.5k Dec 31, 2022
Neural Nano-Optics for High-quality Thin Lens Imaging

Neural Nano-Optics for High-quality Thin Lens Imaging Project Page | Paper | Data Ethan Tseng, Shane Colburn, James Whitehead, Luocheng Huang, Seung-H

Ethan Tseng 39 Dec 05, 2022