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🔥3D点云目标检测&语义分割(深度学习)-SOTA方法,代码,论文,数据集等
Writing AI Conference Papers: A Handbook for Beginners
关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记
This is the code related to "Geometry and Uncertainty-Aware 3D Point Cloud Class-Incremental Semantic Segmentation" (CVPR 2023)
[CVPR 2024] GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding
[CVPR'24 Oral] Official repository of Point Transformer V3 (PTv3)
This is the official repo for Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation (ICCV 23).
The research project based on Semantic KITTTI dataset, 3d Point Cloud Segmentation , Obstacle Detection
A shift-window based transformer for 3D sparse tasks
[ICLR'24 Spotlight] Uni3D: 3D Visual Representation from BAAI
An easy-to-use Python library for processing and manipulating 3D point clouds and meshes.
visual point clouds (with bbox) by Plotly
[NeurIPS'23 Spotlight] Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Official code for "Top-Down Visual Attention from Analysis by Synthesis" (CVPR 2023 highlight)
DeepVision3D is an open source toolbox for point-cloud understanding.
A library for efficient similarity search and clustering of dense vectors.
Code for rendering the point cloud figures in paper: "PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flows"
[TPAMI 2023] RoReg: Pairwise Point Cloud Registration with Oriented Descriptors and Local Rotations
3D graph visualization with WebGL / Three.js
Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
A collection of resources and papers on Diffusion Models
[CVPR 2022] AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation
🤘 awesome-semantic-segmentation
A resource repository for 3D machine learning
程序员在家做饭方法指南。Programmer's guide about how to cook at home (Simplified Chinese only).
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners