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Point Cloud Segmentation
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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.
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Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments
OpenMaskDINO3D : Reasoning 3D Segmentation via Large Language Model
Point Cloud Segmentation of Agricultural Vehicles using 3D Gaussian Splatting
APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Clouds
WLTCL: Wide Field-of-View 3-D LiDAR Truck Compartment Automatic Localization System
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic Gap
ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation
Robust Unsupervised Domain Adaptation for 3D Point Cloud Segmentation Under Source Adversarial Attacks
DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation
GeoT: Geometry-guided Instance-dependent Transition Matrix for Semi-supervised Tooth Point Cloud Segmentation
Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model
Depth-Aware Range Image-Based Model for Point Cloud Segmentation
Deep Unsupervised Segmentation of Log Point Clouds
Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branches
Label-Efficient LiDAR Semantic Segmentation with 2D-3D Vision Transformer Adapters
Explainable LiDAR 3D Point Cloud Segmentation and Clustering for Detecting Airplane-Generated Wind Turbulence
PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured Environments
An Experimental Study of SOTA LiDAR Segmentation Models
Ground Awareness in Deep Learning for Large Outdoor Point Cloud Segmentation
3DSES: an indoor Lidar point cloud segmentation dataset with real and pseudo-labels from a 3D model
LiDAR-Based Vehicle Detection and Tracking for Autonomous Racing
The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challenge
MRG: A Multi-Robot Manufacturing Digital Scene Generation Method Using Multi-Instance Point Cloud Registration
Impact of color and mixing proportion of synthetic point clouds on semantic segmentation
The Bare Necessities: Designing Simple, Effective Open-Vocabulary Scene Graphs
Textured As-Is BIM via GIS-informed Point Cloud Segmentation
Towards Cross-device and Training-free Robotic Grasping in 3D Open World
BelHouse3D: A Benchmark Dataset for Assessing Occlusion Robustness in 3D Point Cloud Semantic Segmentation
Multiscale Graph Construction Using Non-local Cluster Features
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model
Automated Image-Based Identification and Consistent Classification of Fire Patterns with Quantitative Shape Analysis and Spatial Location Identification
Multimodality Helps Few-Shot 3D Point Cloud Semantic Segmentation
Exploring contextual modeling with linear complexity for point cloud segmentation
Generalizable Humanoid Manipulation with 3D Diffusion Policies
Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy
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