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3D Semantic Segmentation

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하위 태스크: 5
평가표: 19
보존된 평가 기록: 145

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/AIS-Bonn/lattice_net

공식 코드

LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices

https://github.com/AnTao97/LGM

공식 코드

Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network

https://github.com/AnTao97/SegGroup

공식 코드

SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation

https://github.com/DavidGillsjo/bssc-net

공식 코드

In Depth Bayesian Semantic Scene Completion

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

공식 코드

KPRNet: Improving projection-based LiDAR semantic segmentation

https://github.com/GAP-LAB-CUHK-SZ/TO-Scene

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TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes

https://github.com/GangZhang842/CPGNet

공식 코드

CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation

https://github.com/HannahHaensen/pyfu

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Deep Sensor Fusion with Pyramid Fusion Networks for 3D Semantic Segmentation

https://github.com/HuguesTHOMAS/KPConv

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KPConv: Flexible and Deformable Convolution for Point Clouds

https://github.com/ICEORY/PMF

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EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation

https://github.com/KangchengLiu/FAC_Foreground_Aware_Contrast

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Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast

https://github.com/KangchengLiu/Feature-Geometric-Net-FG-Net

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FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling

https://github.com/MingyeXu/IAF-Net

공식 코드

Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud

https://github.com/Nathan-UW/OpenGF

공식 코드

OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World

https://github.com/Nightmare-n/UniPAD

공식 코드

UniPAD: A Universal Pre-training Paradigm for Autonomous Driving

https://github.com/ORippler/MSD_2018

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AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018

https://github.com/OpenGVLab/PonderV2

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PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

https://github.com/POSTECH-CVLab/FastPointTransformer

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Fast Point Transformer

https://github.com/PRBonn/semantic-kitti-api

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SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

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/Project-MONAI/research-contributions/tree/master/SwinUNETR/BRATS21

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Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

https://github.com/QingyongHu/RandLA-Net

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RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

https://github.com/RozDavid/LanguageGroundedSemseg

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Language-Grounded Indoor 3D Semantic Segmentation in the Wild

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/Semanticity-Research/SRBIM

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Towards Automating the Retrospective Generation of BIM Models: A Unified Framework for 3D Semantic Reconstruction of the Built Environment

https://github.com/Shathe/3D-MiniNet

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3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

https://github.com/ShiQiu0419/BAAF-Net

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Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion

https://github.com/StanfordVL/MinkowskiEngine

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4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

https://github.com/TRAILab/ST-SLidR

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Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss

https://github.com/TiagoCortinhal/SalsaNext

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SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

https://github.com/UniLauX/PALNet

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Depth Based Semantic Scene Completion with Position Importance Aware Loss

https://github.com/VisualComputingInstitute/3d-semantic-segmentation

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Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds

https://github.com/VisualComputingInstitute/DITR

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

https://github.com/VisualComputingInstitute/dcm-net

공식 코드

DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes

https://github.com/WXinlong/ASIS

공식 코드

Associatively Segmenting Instances and Semantics in Point Clouds

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