daihengming

daihengming's Stars

• facebookresearch/segment-anything

  The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

  Language:Jupyter Notebook45.4k3026585.4k

• tomgoldstein/loss-landscape

  Code for visualizing the loss landscape of neural nets

  Language:Python2.7k3241388

• traveller59/spconv

  Spatial Sparse Convolution Library

  Language:Python1.8k23680359

• xinychen/awesome-latex-drawing

  Drawing Bayesian networks, graphical models, tensors, technical frameworks, and illustrations in LaTeX.

  Language:TeX1.2k361155

• whutug/whu-thesis

  :memo: 武汉大学毕业论文 LaTeX 模版 2024

  Language:TeX90711146200

• NUAA-AL/ALiPy

  ALiPy: Active Learning in Python is an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods.

  Language:Python8632834115

• tusen-ai/SST

  Code for a series of work in LiDAR perception, including SST (CVPR 22), FSD (NeurIPS 22), FSD++ (TPAMI 23), FSDv2, and CTRL (ICCV 23, oral).

  Language:Python7581518395

• SupeRuier/awesome-active-learning

  Everything you need about Active Learning (AL).

  75611273

• HuguesTHOMAS/KPConv-PyTorch

  Kernel Point Convolution implemented in PyTorch

  Language:Python73815236149

• antao97/dgcnn.pytorch

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

  Language:Python677990143

• luost26/diffusion-point-cloud

  :thought_balloon: Diffusion Probabilistic Models for 3D Point Cloud Generation (CVPR 2021)

  Language:Python61393885

• qinzheng93/GeoTransformer

  [CVPR2022] Geometric Transformer for Fast and Robust Point Cloud Registration

  Language:Python5811210963

• mit-han-lab/spvnas

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

  Language:Python5782499109

• TiagoCortinhal/SalsaNext

  Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

  Language:Python3951968103

• yanx27/2DPASS

  2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds (ECCV 2022) :fire:

  Language:Python390127752

• huaaaliu/RGBX_Semantic_Segmentation

  Language:Python28684527

• POSTECH-CVLab/FastPointTransformer

  Official source code of Fast Point Transformer, CVPR 2022

  Language:Python26261741

• chaytonmin/Occupancy-MAE

  Official implementation of our TIV'23 paper: Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

  Language:Python24173020

• xiaoaoran/3d_url_survey

  (TPAMI2023) Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey

  18714120

• dvlab-research/Context-Aware-Consistency

  Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

  Language:Python15553019

• jialeli1/lidarseg3d

  A repository for LiDAR 3D semantic segmentation in autonomous driving scenarios. Also the official implementations of our ECCV 2022 paper (SDSeg3D) and CVPR 2023 paper (MSeg3D).

  Language:Python1155119

• Nightmare-n/GD-MAE

  GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Clouds (CVPR 2023)

  Language:Python1077237

• georghess/voxel-mae

  Code for the paper "Masked Autoencoders for Self-Supervised Learning on Automotive Point Clouds"

  Language:Python743157

• qinzheng93/Easy-KPConv

  A more easy-to-use implementation of KPConv

  Language:Python674510

• sandialabs/bcnn

  3D Bayesian Convolutional Neural Network (BCNN) for Credible Geometric Uncertainty. Code for the paper: https://arxiv.org/abs/1910.10793

  Language:Python597319

• YingzhenLi/Dropout_BBalpha

  Implementations of the ICML 2017 paper (with Yarin Gal)

  Language:Python395011

• ZhangLingMing1/TSGCNet

  Language:Python3421110

• hzykent/LiDAL

  Implementation of ECCV2022 paper - LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation

  Language:Python31241

• weecology/NeonTreeEvaluation_package

  R package for evaluating individual tree crown predictions against a diverse benchmark dataset

  Language:R206346

• whuwuteng/benchmark_ISPRS2021

  15330