Papers with Code 아카이브
3D Semantic Segmentation
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3D Semantic Segmentation is a computer vision task that involves dividing a 3D point cloud or 3D mesh into semantically meaningful parts or regions. The goal of 3D semantic segmentation is to identify and label different objects and parts within a 3D scene, which can be used for applications such as robotics, autonomous driving, and augmented reality.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lightaime/deep_gcns
공식 코드https://github.com/lightaime/deep_gcns_torch
공식 코드https://github.com/linhaojia13/pointmetabase
공식 코드https://github.com/liuzhengzhe/One-Thing-One-Click
공식 코드One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation
https://github.com/ljollans/trunet
공식 코드Vision Transformers increase efficiency of 3D cardiac CT multi-label segmentation
https://github.com/loicland/superpoint_graph
공식 코드Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
https://github.com/lordzth666/WACV23_PIDS-Joint-Point-Interaction-Dimension-Search-for-3D-Point-Cloud
공식 코드PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
https://github.com/lqzhao/SAFNet
공식 코드Similarity-Aware Fusion Network for 3D Semantic Segmentation
https://github.com/luzaijiaoxial/hrhd-hk
공식 코드https://github.com/madanela/smaller3d
공식 코드https://github.com/marios2019/CSN
공식 코드Cross-Shape Attention for Part Segmentation of 3D Point Clouds
https://github.com/mcg-nju/link
공식 코드https://github.com/meidachen/STPLS3D
공식 코드STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
https://github.com/mit-han-lab/pvcnn
공식 코드https://github.com/mit-han-lab/spvnas
공식 코드Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/neheller/kits19
공식 코드https://github.com/nerminsamet/seedal
공식 코드https://github.com/neuronflow/blob_loss
공식 코드blob loss: instance imbalance aware loss functions for semantic segmentation
https://github.com/nexer8/se3d
공식 코드SE3D: A Framework For Saliency Method Evaluation In 3D Imaging
https://github.com/octree-nn/octformer
공식 코드https://github.com/ouenal/scribblekitti
공식 코드https://github.com/pierlui92/Shooting-Labels
공식 코드https://github.com/pjlab-adg/pcseg
공식 코드UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
https://github.com/placeforyiming/iros21-fidnet-semantickitti
공식 코드FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding
https://github.com/plusmultiply/mprm
공식 코드Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds
https://github.com/pointcept/pointtransformerv3
공식 코드https://github.com/pqhieu/JSIS3D
공식 코드https://github.com/prbonn/3diss
공식 코드Towards Generating Realistic 3D Semantic Training Data for Autonomous Driving
https://github.com/qcraftai/cross-modal-ssl
공식 코드Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds
https://github.com/runnanchen/clip2scene
공식 코드CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
https://github.com/sharpershape/eclair-dataset
공식 코드ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation
https://github.com/shuhongll/sgs-slam
공식 코드https://github.com/simonbuusjensen/opentrench3d
공식 코드https://github.com/sj-li/minet
공식 코드Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform
https://github.com/songw-zju/Meta-RangeSeg
공식 코드Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.