Implementation of CVPR'2022:Surface Reconstruction from Point Clouds by Learning Predictive Context Priors

Last update: Dec 12, 2022

Overview

Surface Reconstruction from Point Clouds by Learning Predictive Context Priors (CVPR 2022)

Personal Web Pages | Paper | Project Page

This repository contains the code to reproduce the results from the paper. [Surface Reconstruction from Point Clouds by Learning Predictive Context Priors](https://arxiv.org/abs/2204.11015).

You can find detailed usage instructions for training your own models and using pretrained models below.

If you find our code or paper useful, please consider citing

@inproceedings{PredictiveContextPriors,
    title = {Surface Reconstruction from Point Clouds by Learning Predictive Context Priors},
    author = {Baorui, Ma and Yu-Shen, Liu and Matthias, Zwicker and Zhizhong, Han},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2022}
}

Surface Reconstruction Demo

Predicted Queries Visualization

Predicted queries in Loccal Coorinate System

Installation

First you have to make sure that you have all dependencies in place. The simplest way to do so, is to use anaconda.

You can create an anaconda environment called tf using

conda env create -f tf.yaml
conda activate tf

Training

You should train the Local Context Prior Network first, run

python pcp.py --input_ply_file test.ply --data_dir ./data/ --CUDA 0 --OUTPUT_DIR_LOCAL ./local_net/ --OUTPUT_DIR_GLOBAL ./glocal_net/ --train --save_idx -1

You should put the point cloud file(--input_ply_file, only ply format) into the '--data_dir' folder.

Then train the Predictive Context Prior Network, run

python pcp.py --input_ply_file test.ply --data_dir ./data/ --CUDA 0 --OUTPUT_DIR_LOCAL ./local_net/ --OUTPUT_DIR_GLOBAL ./glocal_net/ --finetune --save_idx -1

Test

You can extract the mesh model from the trained network, run

python pcp.py --input_ply_file test.ply --data_dir ./data/ --CUDA 0 --OUTPUT_DIR_LOCAL ./local_net/ --OUTPUT_DIR_GLOBAL ./glocal_net/ --test --save_idx -1

Implementation of CVPR'2022:Surface Reconstruction from Point Clouds by Learning Predictive Context Priors

Related tags

Overview

Surface Reconstruction from Point Clouds by Learning Predictive Context Priors (CVPR 2022)

Personal Web Pages | Paper | Project Page

Surface Reconstruction Demo

Predicted Queries Visualization

Predicted queries in Loccal Coorinate System

Installation

Training

Test

Owner

PyTorch implementation of deep GRAph Contrastive rEpresentation learning (GRACE).

CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022)

Code for Contrastive-Geometry Networks for Generalized 3D Pose Transfer

MoCoGAN: Decomposing Motion and Content for Video Generation

End-to-End Object Detection with Fully Convolutional Network

A Decentralized Omnidirectional Visual-Inertial-UWB State Estimation System for Aerial Swar.

AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning

Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.

Efficient Sparse Attacks on Videos using Reinforcement Learning

Artifacts for paper "MMO: Meta Multi-Objectivization for Software Configuration Tuning"

Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.

Zero-shot Learning by Generating Task-specific Adapters

some classic model used to segment the medical images like CT、X-ray and so on

Semi-supervised Implicit Scene Completion from Sparse LiDAR

WaveFake: A Data Set to Facilitate Audio DeepFake Detection

CATE: Computation-aware Neural Architecture Encoding with Transformers

CIFAR-10 Photo Classification

🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

Pytorch implementation of Implicit Behavior Cloning.

torchsummaryDynamic: support real FLOPs calculation of dynamic network or user-custom PyTorch ops