SOTAVerified

Point Cloud Segmentation

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 272 papers

TitleStatusHype
Fast 3D Line Segment Detection From Unorganized Point CloudCode0
PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured EnvironmentsCode0
Oriented Point Sampling for Plane Detection in Unorganized Point CloudsCode0
On Universal Equivariant Set NetworksCode0
On the Over-Smoothing Problem of CNN Based Disparity EstimationCode0
A Dataset for Analysing Complex Document Layouts in the Digital Humanities and Its Evaluation with Krippendorff’s AlphaCode0
pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud SegmentationCode0
Rethinking 3D LiDAR Point Cloud SegmentationCode0
cilantro: A Lean, Versatile, and Efficient Library for Point Cloud Data ProcessingCode0
Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point CloudsCode0
Multi-modality Affinity Inference for Weakly Supervised 3D Semantic SegmentationCode0
3D-model ShapeNet Core Classification using Meta-Semantic LearningCode0
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud SegmentationCode0
MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic MonitoringCode0
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic GapCode0
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable TerrainCode0
Boundary-Aware Geometric Encoding for Semantic Segmentation of Point CloudsCode0
LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D ImagesCode0
Projection-based Point Convolution for Efficient Point Cloud SegmentationCode0
Label Name is Mantra: Unifying Point Cloud Segmentation across Heterogeneous Datasets—0
Depth-Aware Range Image-Based Model for Point Cloud Segmentation—0
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model—0
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach—0
Label-Efficient LiDAR Semantic Segmentation with 2D-3D Vision Transformer Adapters—0
Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OcCo-PCNmean Corruption Error (mCE)1.17—Unverified
2OcCo-PointNetmean Corruption Error (mCE)1.13—Unverified
3PointNet++mean Corruption Error (mCE)1.11—Unverified
4PointTransformersmean Corruption Error (mCE)1.05—Unverified
5PointMLPmean Corruption Error (mCE)0.98—Unverified
6PointMAEmean Corruption Error (mCE)0.93—Unverified
7GDANetmean Corruption Error (mCE)0.92—Unverified
8GDANetmean Corruption Error (mCE)0.89—Unverified