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

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Showing 101–150 of 272 papers

TitleStatusHype
PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured EnvironmentsCode0
A Dataset for Analysing Complex Document Layouts in the Digital Humanities and Its Evaluation with Krippendorff’s AlphaCode0
Impact of color and mixing proportion of synthetic point clouds on semantic segmentationCode0
Fast 3D Line Segment Detection From Unorganized Point CloudCode0
pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud SegmentationCode0
Scalable Certified Segmentation via Randomized SmoothingCode0
MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic MonitoringCode0
Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point CloudsCode0
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable TerrainCode0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
Oriented Point Sampling for Plane Detection in Unorganized Point CloudsCode0
Segmentation of EM showers for neutrino experiments with deep graph neural networksCode0
T-MAE: Temporal Masked Autoencoders for Point Cloud Representation LearningCode0
Toward better boundary preserved supervoxel segmentation for 3D point cloudsCode0
cilantro: A Lean, Versatile, and Efficient Library for Point Cloud Data ProcessingCode0
A Unified Point-Based Framework for 3D SegmentationCode0
Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental ModelCode0
Towards Robust Few-shot Point Cloud Semantic SegmentationCode0
LatticeNet: Fast Point Cloud Segmentation Using Permutohedral LatticesCode0
SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel—0
Language-Level Semantics Conditioned 3D Point Cloud Segmentation—0
SegPoint: Segment Any Point Cloud via Large Language Model—0
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning—0
Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models—0
Semantically Adversarial Scenario Generation with Explicit Knowledge Guidance—0
Semantic Context Encoding for Accurate 3D Point Cloud Segmentation—0
Semantic Segmentation of Surface from Lidar Point Cloud—0
Sequential Point Clouds: A Survey—0
Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model—0
Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly—0
Stereo Frustums: A Siamese Pipeline for 3D Object Detection—0
TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series—0
Textured As-Is BIM via GIS-informed Point Cloud Segmentation—0
The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challenge—0
The Bare Necessities: Designing Simple, Effective Open-Vocabulary Scene Graphs—0
Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciences—0
Towards Cross-device and Training-free Robotic Grasping in 3D Open World—0
Traffic Sign Timely Visual Recognizability Evaluation Based on 3D Measurable Point Clouds—0
Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation—0
TSDASeg: A Two-Stage Model with Direct Alignment for Interactive Point Cloud Segmentation—0
Twin Deformable Point Convolutions for Point Cloud Semantic Segmentation in Remote Sensing Scenes—0
U3DS^3: Unsupervised 3D Semantic Scene Segmentation—0
UAV LiDAR Point Cloud Segmentation of A Stack Interchange with Deep Neural Networks—0
Uncertainty Estimation in Deep Neural Networks for Point Cloud Segmentation in Factory Planning—0
Underground Mapping and Localization Based on Ground-Penetrating Radar—0
Uplifting Range-View-based 3D Semantic Segmentation in Real-Time with Multi-Sensor Fusion—0
Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models—0
VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars—0
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning—0
Weakly Supervised Pseudo-Label assisted Learning for ALS Point Cloud Semantic 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