SOTAVerified

3D Semantic Segmentation

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.

Papers

Showing 110 of 348 papers

TitleStatusHype
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point CloudsCode1
GS4: Generalizable Sparse Splatting Semantic SLAM0
Point-MoE: Towards Cross-Domain Generalization in 3D Semantic Segmentation via Mixture-of-Experts0
seg_3D_by_PC2D: Multi-View Projection for Domain Generalization and Adaptation in 3D Semantic SegmentationCode0
MFSeg: Efficient Multi-frame 3D Semantic Segmentation0
3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation0
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding0
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation0
RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration0
Overlap-Aware Feature Learning for Robust Unsupervised Domain Adaptation for 3D Semantic Segmentation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CSNmIOU62.1Unverified
2MID-NetmIOU60.8Unverified
3FG-NetmIOU58.2Unverified
4closerlook3DmIOU53.8Unverified
5DeepGCNmIOU45.1Unverified
6PartNetmIOU43.2Unverified