[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

Related tags

Deep Learningcomputer-visiondeep-learning3dvisionpointcloud-completionvision-transformersiccv2021
Overview

PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

PWC

Created by Xumin Yu*, Yongming Rao*, Ziyi Wang, Zuyan Liu, Jiwen Lu, Jie Zhou

This repository contains PyTorch implementation for PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers (ICCV 2021 Oral Presentation) [arXiv].

PoinTr is a transformer-based model for point cloud completion. By representing the point cloud as a set of unordered groups of points with position embeddings, we convert the point cloud to a sequence of point proxies and employ a transformer encoder-decoder architecture for generation. We also propose two more challenging benchmarks ShapeNet-55/34 with more diverse incomplete point clouds that can better reflect the real-world scenarios to promote future research.

intro

Pretrained Models

We provide pretrained PoinTr models:

dataset url
ShapeNet-55 [Tsinghua Cloud] / [Google Drive] / [BaiDuYun] (code:erdh)
ShapeNet-34 [Tsinghua Cloud] / [Google Drive] / [BaiDuYun] (code:atbb )
PCN [Tsinghua Cloud] / [Google Drive] / [BaiDuYun] (code:9g79)
KITTI coming soon

Usage

Requirements

  • PyTorch >= 1.7.0
  • python >= 3.7
  • CUDA >= 9.0
  • GCC >= 4.9
  • torchvision
  • timm
  • open3d
  • tensorboardX
pip install -r requirements.txt

Building Pytorch Extensions for Chamfer Distance, PointNet++ and kNN

NOTE: PyTorch >= 1.7 and GCC >= 4.9 are required.

# Chamfer Distance
bash install.sh
# PointNet++
pip install "git+git://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"
# GPU kNN
pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl

Dataset

The details of our new ShapeNet-55/34 datasets and other existing datasets can be found in DATASET.md.

Evaluation

To evaluate a pre-trained PoinTr model on the Three Dataset with single GPU, run:

bash ./scripts/test.sh <GPU_IDS> --ckpts <path> --config <config> --exp_name <name> [--mode <easy/median/hard>]

Some examples:

Test the PoinTr pretrained model on the PCN benchmark:

bash ./scripts/test.sh 0 --ckpts ./pretrained/PoinTr_PCN.pth --config ./cfgs/PCN_models/PoinTr.yaml --exp_name example

Test the PoinTr pretrained model on ShapeNet55 benchmark (easy mode):

bash ./scripts/test.sh 0 --ckpts ./pretrained/PoinTr_ShapeNet55.pth --config ./cfgs/ShapeNet55_models/PoinTr.yaml --mode easy --exp_name example

Test the PoinTr pretrained model on the KITTI benchmark:

bash ./scripts/test.sh 0 --ckpts ./pretrained/PoinTr_KITTI.pth --config ./cfgs/KITTI_models/PoinTr.yaml --exp_name example

Training

To train a point cloud completion model from scratch, run:

# Use DistributedDataParallel (DDP)
bash ./scripts/dist_train.sh <NUM_GPU> <port> --config <config> --exp_name <name> [--resume] [--start_ckpts <path>] [--val_freq <int>]
# or just use DataParallel (DP)
bash ./scripts/train.sh <GPUIDS> --config <config> --exp_name <name> [--resume] [--start_ckpts <path>] [--val_freq <int>]

Some examples:

Train a PoinTr model on PCN benchmark with 2 gpus:

CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/dist_train.sh 2 13232 --config ./cfgs/PCN_models/PoinTr.yaml --exp_name example

Resume a checkpoint:

CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/dist_train.sh 2 13232 --config ./cfgs/PCN_models/PoinTr.yaml --exp_name example --resume

Finetune a PoinTr on PCNCars

CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/dist_train.sh 2 13232 --config ./cfgs/KITTI_models/PoinTr.yaml --exp_name example --start_ckpts ./weight.pth

Train a PoinTr model with a single GPU:

bash ./scripts/train.sh 0 --config ./cfgs/KITTI_models/PoinTr.yaml --exp_name example

We also provide the Pytorch implementation of several baseline models including GRNet, PCN, TopNet and FoldingNet. For example, to train a GRNet model on ShapeNet-55, run:

CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/dist_train.sh 2 13232 --config ./cfgs/ShapeNet55_models/GRNet.yaml --exp_name example

Completion Results on ShapeNet55 and KITTI-Cars

results

License

MIT License

Acknowledgements

Our code is inspired by GRNet and mmdetection3d.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{yu2021pointr,
  title={PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers},
  author={Yu, Xumin, Rao, Yongming and Wang, Ziyi and Liu, Zuyan, and Lu, Jiwen and Zhou, Jie},
  booktitle={ICCV},
  year={2021}
}
Owner
Xumin Yu
Xumin Yu
GitHub Repository
A fuzzing framework for SMT solvers

yinyang A fuzzing framework for SMT solvers. Given a set of seed SMT formulas, yinyang generates mutant formulas to stress-test SMT solvers. yinyang c

Project Yin-Yang for SMT Solver Testing 145 Jan 04, 2023
Grammar Induction using a Template Tree Approach

Gitta Gitta ("Grammar Induction using a Template Tree Approach") is a method for inducing context-free grammars. It performs particularly well on data

Thomas Winters 36 Nov 15, 2022
Semantic Scholar's Author Disambiguation Algorithm & Evaluation Suite

S2AND This repository provides access to the S2AND dataset and S2AND reference model described in the paper S2AND: A Benchmark and Evaluation System f

AI2 54 Nov 28, 2022
PRTR: Pose Recognition with Cascade Transformers

PRTR: Pose Recognition with Cascade Transformers Introduction This repository is the official implementation for Pose Recognition with Cascade Transfo

mlpc-ucsd 133 Dec 30, 2022
Anagram Generator in Python

Anagrams Generator This is a program for computing multiword anagrams. It makes no effort to come up with sentences that make sense; it only finds ana

Day Fundora 5 Nov 17, 2022
Code for generating the figures in the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?"

Code for running simulations for the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Lin

Matthew Farrell 1 Nov 22, 2022
Real-time analysis of intracranial neurophysiology recordings.

py_neuromodulation Click this button to run the "Tutorial ML with py_neuro" notebooks: The py_neuromodulation toolbox allows for real time capable pro

Interventional Cognitive Neuromodulation - Neumann Lab Berlin 15 Nov 03, 2022
A very short and easy implementation of Quantile Regression DQN

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

Arsenii Senya Ashukha 80 Sep 17, 2022
SpecAugmentPyTorch - A Pytorch (support batch and channel) implementation of GoogleBrain's SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

SpecAugment An implementation of SpecAugment for Pytorch How to use Install pytorch, version=1.9.0 (new feature (torch.Tensor.take_along_dim) is used

IMLHF 3 Oct 11, 2022
Code of paper Interact, Embed, and EnlargE (IEEE): Boosting Modality-specific Representations for Multi-Modal Person Re-identification.

Interact, Embed, and EnlargE (IEEE): Boosting Modality-specific Representations for Multi-Modal Person Re-identification We provide the codes for repr

12 Dec 12, 2022
End-to-end speech secognition toolkit

End-to-end speech secognition toolkit This is an E2E ASR toolkit modified from Espnet1 (version 0.9.9). This is the official implementation of paper:

Jinchuan Tian 147 Dec 28, 2022
Source code for CIKM 2021 paper for Relation-aware Heterogeneous Graph for User Profiling

RHGN Source code for CIKM 2021 paper for Relation-aware Heterogeneous Graph for User Profiling Dependencies torch==1.6.0 torchvision==0.7.0 dgl==0.7.1

Big Data and Multi-modal Computing Group, CRIPAC 6 Nov 29, 2022
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation. (CVPR 2021)

GDR-Net This repo provides the PyTorch implementation of the work: Gu Wang, Fabian Manhardt, Federico Tombari, Xiangyang Ji. GDR-Net: Geometry-Guided

169 Jan 07, 2023
Experiments for Fake News explainability project

fake-news-explainability Experiments for fake news explainability project This repository only contains the notebooks used to train the models and eva

Lorenzo Flores (Lj) 1 Dec 03, 2022
Codes for "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation"

CSDI This is the github repository for the NeurIPS 2021 paper "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

106 Jan 04, 2023
TensorLight - A high-level framework for TensorFlow

TensorLight is a high-level framework for TensorFlow-based machine intelligence applications. It reduces boilerplate code and enables advanced feature

Benjamin Kan 10 Jul 31, 2022
Neural Scene Flow Fields using pytorch-lightning, with potential improvements

nsff_pl Neural Scene Flow Fields using pytorch-lightning. This repo reimplements the NSFF idea, but modifies several operations based on observation o

AI葵 178 Dec 21, 2022
HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision

HugsVision is an open-source and easy to use all-in-one huggingface wrapper for computer vision. The goal is to create a fast, flexible and user-frien

Labrak Yanis 166 Nov 27, 2022
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

SimplePose Code and pre-trained models for our paper, “Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation”, a

Jia Li 256 Dec 24, 2022
Github for the conference paper GLOD-Gaussian Likelihood OOD detector

FOOD - Fast OOD Detector Pytorch implamentation of the confernce peper FOOD arxiv link. Abstract Deep neural networks (DNNs) perform well at classifyi

17 Jun 19, 2022