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Point Cloud Segmentation

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보존된 평가 기록: 11

3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. The segmentation is challenging because of high redundancy, uneven sampling density, and lack explicit structure of point cloud data. This problem has many applications in robotics such as intelligent vehicles, autonomous mapping and navigation.

Source: 3D point cloud segmentation: A survey

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AIS-Bonn/lattice_net

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LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices

https://github.com/CUHK-AIM-Group/GeoT

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GeoT: Geometry-guided Instance-dependent Transition Matrix for Semi-supervised Tooth Point Cloud Segmentation

https://github.com/CVMI-Lab/PAConv

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PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

https://github.com/DeyvidKochanov-TomTom/kprnet

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KPRNet: Improving projection-based LiDAR semantic segmentation

https://github.com/GeoAI-Research-Lab/PCTFusion

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pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud Segmentation

https://github.com/GuanRunwei/Achelous

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Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar

https://github.com/Hippogriff/prifit

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PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation

https://github.com/LiyaoTang/ERDA

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Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution Alignment

https://github.com/MMCheng/SSPC-Net

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SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network

https://github.com/NimrodSegol/On-Universal-Equivariant-Set-Networks

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On Universal Equivariant Set Networks

https://github.com/Pang-Yatian/Point-MAE

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Masked Autoencoders for Point Cloud Self-supervised Learning

https://github.com/Pixie8888/GFS-3DSeg_GWs

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Generalized Few-Shot Point Cloud Segmentation Via Geometric Words

https://github.com/Pixie8888/R3DFSSeg

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Towards Robust Few-shot Point Cloud Semantic Segmentation

https://github.com/Pointcept/PointTransformerV2

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Point Transformer V2: Grouped Vector Attention and Partition-based Pooling

https://github.com/Pointcept/Pointcept

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Point Transformer

https://github.com/QingyongHu/SoTA-Point-Cloud

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Deep Learning for 3D Point Clouds: A Survey

https://github.com/RikkiXu/NCD_PC

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Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentation

https://github.com/Semanticity-Research/ARCH2S

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ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point Clouds

https://github.com/ShiQiu0419/DRNet

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Dense-Resolution Network for Point Cloud Classification and Segmentation

https://github.com/VITA-Group/Simple3D-Former

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Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?

https://github.com/VSlabRobotics/PFSD

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PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured Environments

https://github.com/VisualComputingInstitute/DITR

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DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation

https://github.com/WangYueFt/dgcnn

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Dynamic Graph CNN for Learning on Point Clouds

https://github.com/Yacovitch/EyeNet

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Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene

https://github.com/YanjieZe/Improved-3D-Diffusion-Policy

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Generalizable Humanoid Manipulation with 3D Diffusion Policies

https://github.com/aaronnzh/boosting-few-shot-3d-point-cloud-segmentation-via-query-guided-enhancement

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Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement

https://github.com/alex-xun-xu/WeakSupPointCloudSeg

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Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels

https://github.com/ananyajana/single_dental_model_learning

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Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental Model

https://github.com/azuki-miho/RFCR

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Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning

https://github.com/bizerfr/bpnet

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BPNet: Bézier Primitive Segmentation on 3D Point Clouds

https://github.com/brian-h-wang/LDLS

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LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images

https://github.com/c0ldstudy/pointsecguard

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On Adversarial Robustness of Point Cloud Semantic Segmentation

https://github.com/chenfengxu714/SqueezeSegV3

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SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation

https://github.com/codename1995/t-mae

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T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning

https://github.com/cszyzhang/riconv2

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RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning

https://github.com/cv-rits/coarse3d

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COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation

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