Papers 3D Semantic Segmentation
“3D Semantic Segmentation” 태그가 달린 논문 391편 · 필터 해제
VLRC: Vision-Language Reprojection Consistency as a scalable signal for better feed-forward 3D pretraining
Feed-forward 3D models are commonly trained using either expensive geometric supervision or self-supervised photometric objectives, both of which provide incomplete learning signals. We introduce Vision-Language Reprojec…
3D Semantic SegmentationScene Understanding3D ReconstructionPrivacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization
Privacy-preserving perception is a critical requirement for deploying 3D scene understanding systems in real-world indoor environments, yet it remains underexplored in open-vocabulary 3D semantic segmentation. Existing m…
3D Semantic SegmentationScene UnderstandingHeterogeneous and Adept Snapshot Distillation for 3D Semantic Segmentation
Multi-modal fusion and multi-model ensembling are prevalent in enhancing the performance of 3D semantic segmentation. Despite the impressive performance, these methods either rely on auxiliary input signals or suffer fro…
3D Semantic SegmentationKnowledge DistillationPoint CloudsGIBLy: Improving 3D Semantic Segmentation through an Architecture-Agnostic Lightweight Geometric Inductive Bias Layer
In 3D scene understanding, deep learning models rely on large models and extensive training to capture basic geometric structures that are present in the 3D data. However, existing methods lack explicit mechanisms to inc…
3D Semantic SegmentationScene UnderstandingCollaborative Learning for Semi-Supervised LiDAR Semantic Segmentation
Annotating large-scale LiDAR point clouds for 3D semantic segmentation is costly and time-consuming, which motivates the use of semi-supervised learning (SemiSL). Standard LiDAR SemiSL methods typically adopt a two-step …
LIDAR Semantic Segmentation3D Semantic SegmentationPoint CloudsOPTNet: Ordering Point Transformer Network for Post-disaster 3D Semantic Segmentation
Post-disaster damage assessment requires rapid and accurate semantic segmentation of 3D point clouds to identify critical infrastructure such as damaged buildings and roads. Early Point Transformers (e.g., PTv1, PTv2) re…
3D Semantic SegmentationPoint CloudsUniD-Shift: Towards Unified Semantic Segmentation via Interpretable Share-Private Multimodal Decomposition
Semantic segmentation of large-scale 3D point clouds is crucial for applications such as autonomous driving and urban digital twins. However, the sparse sampling pattern of LiDAR and the view-dependent geometric distorti…
3D Semantic SegmentationComputational EfficiencyAutonomous DrivingPoint CloudsFUS3DMaps: Scalable and Accurate Open-Vocabulary Semantic Mapping by 3D Fusion of Voxel- and Instance-Level Layers
Open-vocabulary semantic mapping enables robots to spatially ground previously unseen concepts without requiring predefined class sets. Current training-free methods commonly rely on multi-view fusion of semantic embeddi…
3D Semantic SegmentationINSIGHT: Indoor Scene Intelligence from Geometric-Semantic Hierarchy Transfer for Public~Safety
Indoor environments lack the spatial intelligence infrastructure that GPS provides outdoors; first responders arriving at unfamiliar buildings typically have no machine-readable map of safety equipment. Prior work on 3D …
3D Semantic SegmentationPoint CloudsPanDA: Unsupervised Domain Adaptation for Multimodal 3D Panoptic Segmentation in Autonomous Driving
This paper presents the first study on Unsupervised Domain Adaptation (UDA) for multimodal 3D panoptic segmentation (mm-3DPS), aiming to improve generalization under domain shifts commonly encountered in real-world auton…
Unsupervised Domain Adaptation3D Semantic SegmentationRepresentation LearningPanoptic SegmentationVolume Transformer: Revisiting Vanilla Transformers for 3D Scene Understanding
Transformers have become a common foundation across deep learning, yet 3D scene understanding still relies on specialized backbones with strong domain priors. This keeps the field isolated from the broader Transformer ec…
3D Semantic Segmentation3D Instance SegmentationScene UnderstandingInstant Colorization of Gaussian Splats
Gaussian Splatting has recently become one of the most popular frameworks for photorealistic 3D scene reconstruction and rendering. While current rasterizers allow for efficient mappings of 3D Gaussian splats onto 2D cam…
3D Semantic SegmentationGeoGuide: Hierarchical Geometric Guidance for Open-Vocabulary 3D Semantic Segmentation
Open-vocabulary 3D semantic segmentation aims to segment arbitrary categories beyond the training set. Existing methods predominantly rely on distilling knowledge from 2D open-vocabulary models. However, aligning 3D feat…
3D Semantic SegmentationSemantic SimilarityBenchmarking Deep Learning Models for Aerial LiDAR Point Cloud Semantic Segmentation under Real Acquisition Conditions: A Case Study in Navarre
Recent advances in deep learning have significantly improved 3D semantic segmentation, but most models focus on indoor or terrestrial datasets. Their behavior under real aerial acquisition conditions remains insufficient…
3D Semantic SegmentationComputational EfficiencyUncertainty-aware Prototype Learning with Variational Inference for Few-shot Point Cloud Segmentation
Few-shot 3D semantic segmentation aims to generate accurate semantic masks for query point clouds with only a few annotated support examples. Existing prototype-based methods typically construct compact and deterministic…
Point Cloud Segmentation3D Semantic SegmentationPoint CloudsJOPP-3D: Joint Open Vocabulary Semantic Segmentation on Point Clouds and Panoramas
Semantic segmentation across visual modalities such as 3D point clouds and panoramic images remains a challenging task, primarily due to the scarcity of annotated data and the limited adaptability of fixed-label models. …
Open Vocabulary Semantic Segmentation3D Semantic SegmentationScene UnderstandingPoint CloudsCoSMo3D: Open-World Promptable 3D Semantic Part Segmentation through LLM-Guided Canonical Spatial Modeling
Open-world promptable 3D semantic segmentation remains brittle as semantics are inferred in the input sensor coordinates. Yet, humans, in contrast, interpret parts via functional roles in a canonical space -- wings exten…
3D Semantic SegmentationXD-MAP: Cross-Modal Domain Adaptation via Semantic Parametric Maps for Scalable Training Data Generation
Until open-world foundation models match the performance of specialized approaches, deep learning systems remain dependent on task- and sensor-specific data availability. To bridge the gap between available datasets and …
2D Semantic Segmentation2D Panoptic Segmentation3D Semantic SegmentationDomain AdaptationGridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure
This paper presents GridNet-HD, a multi-modal dataset for 3D semantic segmentation of overhead electrical infrastructures, pairing high-density LiDAR with high-resolution oblique imagery. The dataset comprises 7,694 imag…
3D Semantic SegmentationDeep Learning for Semantic Segmentation of 3D Ultrasound Data
Developing cost-efficient and reliable perception systems remains a central challenge for automated vehicles. LiDAR and camera-based systems dominate, yet they present trade-offs in cost, robustness and performance under…
3D Semantic Segmentation