Stars
This is a PyTorch implementation of GPSFormer proposed by our paper "GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding"(ECCV2024)
This is a PyTorch implementation of PointMetaBase proposed by our paper "Meta Architecure for Point Cloud Analysis"
HEDNet (NeurIPS 2023) & SAFDNet (CVPR 2024 Oral)
StructureNet: Hierarchical Graph Networks for 3D Shape Generation
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud, CVPR 2020.
[ISPRS2025] SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Model
A list of papers and datasets about point cloud analysis (processing) since 2017. Update every day!
This is the official implementation of the paper - GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training
[CVPR2022] Point Cloud Pre-training with Natural 3D Structures
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation [ICCV 2023]
ICCV2021 (Oral) - Exploring Cross-Image Pixel Contrast for Semantic Segmentation
Pointcept: a codebase for point cloud perception research. Latest works: Sonata (CVPR'25 Highlight), PTv3 (CVPR'24 Oral), PPT (CVPR'24), MSC (CVPR'23)
[CVPR'22 & IJCV'24] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels & Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation
A bunch of lints to catch common mistakes and improve your Rust code. Book: https://doc.rust-lang.org/clippy/
Perceptual Grouping in Contrastive Vision-Language Models (ICCV'23)
A summary of recent semi-supervised semantic segmentation methods
[ICCV'23] Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation
[CVPR2024] VideoBooth: Diffusion-based Video Generation with Image Prompts
Finetuning of VideoMAE on PDEBench (forked from https://github.com/MCG-NJU/VideoMAE)
Official Code for NeurIPS 2022 Paper: How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
Official PyTorch code for ICCV 23' paper Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study