Continuous Conditional Random Field Convolution for Point Cloud Segmentation

Last update: Dec 08, 2022

Related tags

Deep Learning CRFConv

Overview

CRFConv

This repository is the implementation of "Continuous Conditional Random Field Convolution for Point Cloud Segmentation"

1. Setup

1) Building

cd utils
sh compile_op.sh

2) Dependency

This repository is partially dependent on 'pytorch', 'torch_geometric', and 'torch_points3d'.

2. Running

Please see the trainval.py for an example of running with different datasets in the 'dataset' folder.

3. Acknowledgment

Part of the codes refers to the KPConv and RandLA-Net.

4. Others

This repository will continue updating.

Owner

Fei Yang

I am a postgraduate of NJUST. CS. school, major in computer vision and machine learning, mainly do some research on digital image processing.

GitHub Repository

This repository contains the accompanying code for Deep Virtual Markers for Articulated 3D Shapes, ICCV'21

Deep Virtual Markers This repository contains the accompanying code for Deep Virtual Markers for Articulated 3D Shapes, ICCV'21 Getting Started Get sa

45 Oct 07, 2022

Fast Axiomatic Attribution for Neural Networks (NeurIPS*2021)

Fast Axiomatic Attribution for Neural Networks This is the official repository accompanying the NeurIPS 2021 paper: R. Hesse, S. Schaub-Meyer, and S.

11 Nov 21, 2022

Hydra: an Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems

Hydra: An Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems Paper Finding Semantic Bugs in File Systems with an Extensible Fuzzin

[email protected])"> 129 Dec 15, 2022

Speeding-Up Back-Propagation in DNN: Approximate Outer Product with Memory

Approximate Outer Product Gradient Descent with Memory Code for the numerical experiment of the paper Speeding-Up Back-Propagation in DNN: Approximate

2 Mar 02, 2022

Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"

Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision https://arxiv.org/abs/2003.00393 Abstract Active learning (AL) aims to min

29 Nov 21, 2022

Official code of "R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network."

R2RNet Official code of "R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network." Jiang Hai, Zhu Xuan, Ren Yang, Yutong Hao, Fengzhu

77 Dec 24, 2022

A neuroanatomy-based augmented reality experience powered by computer vision. Features 3D visuals of the Atlas Brain Map slices.

Brain Augmented Reality (AR) A neuroanatomy-based augmented reality experience powered by computer vision that features 3D visuals of the Atlas Brain

10 Oct 06, 2022

Details about the wide minima density hypothesis and metrics to compute width of a minima

wide-minima-density-hypothesis Details about the wide minima density hypothesis and metrics to compute width of a minima This repo presents the wide m

9 Dec 27, 2022

PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners for self-supervised ViT.

MAE for Self-supervised ViT Introduction This is an unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners for self-sup

36 Oct 30, 2022

Code and datasets for the paper "KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction"

KnowPrompt Code and datasets for our paper "KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction" Requireme

137 Dec 31, 2022

2021-MICCAI-Progressively Normalized Self-Attention Network for Video Polyp Segmentation

2021-MICCAI-Progressively Normalized Self-Attention Network for Video Polyp Segmentation Authors: Ge-Peng Ji*, Yu-Cheng Chou*, Deng-Ping Fan, Geng Che

85 Dec 30, 2022

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting Recent progress in neural forecasting instigated significant improvements in the

82 Jan 04, 2023

A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading

A tour through tensorflow with financial data I present several models ranging in complexity from simple regression to LSTM and policy networks. The s

195 Dec 07, 2022