331 Open source projects that are alternatives of or similar to PointCutMix

Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance (ECCV2020)

Paper list of deep learning on point clouds.

Augmentation package for 3d data based on albumentaitons

Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)

C++ library and programs for reading and writing ASPRS LAS format with LiDAR data

Building an ACL tear detector to spot knee injuries from MRIs with PyTorch (MRNet)

No description or website provided.

L-CAS 3D Point Cloud Annotation Tool

FlowNet3D: Learning Scene Flow in 3D Point Clouds (CVPR 2019)

The open source mesh processing system

Point cloud semantic segmentation via Deep 3D Convolutional Neural Network

Python bindings for the Point Cloud Library (PCL)

Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features

The PyTorch Implementation of F-ConvNet for 3D Object Detection

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021)

3D Graph Neural Networks for RGBD Semantic Segmentation

[CVPR 2021] Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

A hackable lidar viewer

register 3D point clouds using rotation, translation, and scale transformations.

A high-performance neural network library for point cloud processing.

Code for PCN: Point Completion Network in 3DV'18 (Oral)

MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Robotics with GPU computing

PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling （CVPR 2020）

A PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)

The public CGAL repository, see the README below

[CVPR 2021] Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis (official pytorch implementation)

Differentiable Point Cloud Sampling (CVPR 2020 Oral)

Tensor2tensor experiment with SpecAugment

Code for A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION (ICASSP 2018)

Artificial intelligence/machine learning course at UCF in Spring 2020 (Fall 2019 and Spring 2019)

Implementation of the Point Transformer layer, in Pytorch

An extended TSP (Time Stretched Pulse). CAPRICEP substantially replaces FVN. CAPRICEP enables interactive and real-time measurement of the linear time-invariant, the non-linear time-invariant, and random and time varying responses simultaneously.

orb-slam2 with semantic label

中文NER的那些事儿

Papers and Datasets about Point Cloud.

[Medical Image Analysis] Adversarial Data Augmentation with Chained Transformations (AdvChain)

3D Bounding Box Annotation Tool (3D-BAT) Point cloud and Image Labeling

Implementation of modern data augmentation techniques in TensorFlow 2.x to be used in your training pipeline.

Research platform for 3D object detection in PyTorch.

SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)

Differentiable Surface Splatting

[ECCV 2020] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

DBNet: A Large-Scale Dataset for Driving Behavior Learning, CVPR 2018

Experiments on unsupervised point cloud reconstruction.

Paperlist of awesome 3D detection methods

Associatively Segmenting Instances and Semantics in Point Clouds, CVPR 2019

Python binding of 3D visualization library Pangolin

[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation

An integrated SfM (Structure from Motion) and MVS (Multi-View Stereo) solution.

Rank 1st in the leaderboard of SemanticKITTI semantic segmentation (both single-scan and multi-scan) (Nov. 2020) (CVPR2021 Oral)

Julia bindings for the displaz lidar viewer

Neural Point-Based Graphics

PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition, CVPR 2018

This is a package for extrinsic calibration between a 3D LiDAR and a camera, described in paper: Improvements to Target-Based 3D LiDAR to Camera Calibration. This package is used for Cassie Blue's 3D LiDAR semantic mapping and automation.

[CVPR'19] JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds

Efficient 3D Deep Learning

A ground segmentation algorithm for 3D point clouds based on the work described in “Fast segmentation of 3D point clouds: a paradigm on LIDAR data for Autonomous Vehicle Applications”, D. Zermas, I. Izzat and N. Papanikolopoulos, 2017. Distinguish between road and non-road points. Road surface extraction. Plane fit ground filter