Papers 3D Instance Segmentation
“3D Instance Segmentation” 태그가 달린 논문 187편 · 필터 해제
AQ3D: Adaptive Query Transformer for 3D Instance Segmentation
Transformer-based decoders for 3D instance segmentation typically commit to a fixed number of queries and positional modeling calibrated on the training distribution rather than on the scene at hand. Indoor scans vary wi…
3D Instance SegmentationData AugmentationConsistent Scene Understanding in 3D Gaussian Splatting via Multi-Cue Mask Refinement
Reliable instance-level scene understanding is a fundamental prerequisite for object-level interactions and high-fidelity 3D representations. While current methods often leverage 2D foundation segmentation models to obta…
3D Instance SegmentationScene UnderstandingGVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence
Accurate 3D instance segmentation in point cloud data is critical for machine vision applications. Recent advancements leverage multiple pre-trained foundation models to generate 3D proposals, followed by the application…
3D Instance SegmentationSemantic SegmentationEnsemble LearningESAM++: Efficient Online 3D Perception on the Edge
Online 3D scene perception in real time is essential for robotics, AR/VR, and autonomous systems, particularly in edge computing scenarios where computational resources are limited and privacy is crucial. Recent state-of…
3D Instance SegmentationPoint CloudsEvObj: Learning Evolving Object-centric Representations for 3D Instance Segmentation without Scene Supervision
We introduce EvObj for unsupervised 3D instance segmentation that bridges the geometric domain gap between synthetic pretraining data and real-world point clouds. Current methods suffer from structural discrepancies when…
3D Instance SegmentationObject SegmentationPoint CloudsSpaCeFormer: Fast Proposal-Free Open-Vocabulary 3D Instance Segmentation
Open-vocabulary 3D instance segmentation is a core capability for robotics and AR/VR, but prior methods trade one bottleneck for another: multi-stage 2D+3D pipelines aggregate foundation-model outputs at hundreds of seco…
3D Instance 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 UnderstandingMV3DIS: Multi-View Mask Matching via 3D Guides for Zero-Shot 3D Instance Segmentation
Conventional 3D instance segmentation methods rely on labor-intensive 3D annotations for supervised training, which limits their scalability and generalization to novel objects. Recent approaches leverage multi-view 2D m…
3D Instance SegmentationFAST3DIS: Feed-forward Anchored Scene Transformer for 3D Instance Segmentation
While recent feed-forward 3D reconstruction models provide a strong geometric foundation for scene understanding, extending them to 3D instance segmentation typically relies on a disjointed "lift-and-cluster" paradigm. G…
3D Instance SegmentationRepresentation LearningContrastive LearningScene UnderstandingSegVGGT: Joint 3D Reconstruction and Instance Segmentation from Multi-View Images
3D instance segmentation methods typically rely on high-quality point clouds or posed RGB-D scans, requiring complex multi-stage processing pipelines, and are highly sensitive to reconstruction noise. While recent feed-f…
Multi-View 3D Reconstruction3D Instance SegmentationPoint CloudsIn-Field 3D Wheat Head Instance Segmentation From TLS Point Clouds Using Deep Learning Without Manual Labels
3D instance segmentation for laser scanning (LiDAR) point clouds remains a challenge in many remote sensing-related domains. Successful solutions typically rely on supervised deep learning and manual annotations, and con…
3D Instance SegmentationPoint Cloud SegmentationPoint CloudsField imaging framework for morphological characterization of aggregates with computer vision: Algorithms and applications
Construction aggregates, including sand and gravel, crushed stone and riprap, are the core building blocks of the construction industry. State-of-the-practice characterization methods mainly relies on visual inspection a…
3D Instance Segmentation3D ReconstructionClutt3R-Seg: Sparse-view 3D Instance Segmentation for Language-grounded Grasping in Cluttered Scenes
Reliable 3D instance segmentation is fundamental to language-grounded robotic manipulation. Its critical application lies in cluttered environments, where occlusions, limited viewpoints, and noisy masks degrade perceptio…
3D Instance SegmentationLaSSM: Efficient Semantic-Spatial Query Decoding via Local Aggregation and State Space Models for 3D Instance Segmentation
Query-based 3D scene instance segmentation from point clouds has attained notable performance. However, existing methods suffer from the query initialization dilemma due to the sparse nature of point clouds and rely on c…
3D Instance SegmentationPoint CloudsZ3D: Zero-Shot 3D Visual Grounding from Images
3D visual grounding (3DVG) aims to localize objects in a 3D scene based on natural language queries. In this work, we explore zero-shot 3DVG from multi-view images alone, without requiring any geometric supervision or ob…
3D Instance SegmentationNatural Language QueriesVisual Grounding3AM: 3egment Anything with Geometric Consistency in Videos
Video object segmentation methods like SAM2 achieve strong performance through memory-based architectures but struggle under large viewpoint changes due to reliance on appearance features. Traditional 3D instance segment…
Video Object Segmentation3D Instance SegmentationCropNeRF: A Neural Radiance Field-Based Framework for Crop Counting
Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined with inherent ambiguity in distinguishing …
3D Instance SegmentationUniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning
3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have advanced novel-view synthesis. Recent methods extend multi-view 2D segmentation to 3D, enabling instance/semantic segmentation for better scene understa…
3D Instance SegmentationSemantic SegmentationContrastive LearningScene UnderstandingRetrieving Objects from 3D Scenes with Box-Guided Open-Vocabulary Instance Segmentation
Locating and retrieving objects from scene-level point clouds is a challenging problem with broad applications in robotics and augmented reality. This task is commonly formulated as open-vocabulary 3D instance segmentati…
3D Instance SegmentationPoint CloudsMoonSeg3R: Monocular Online Zero-Shot Segment Anything in 3D with Reconstructive Foundation Priors
In this paper, we focus on online zero-shot monocular 3D instance segmentation, a novel practical setting where existing approaches fail to perform because they rely on posed RGB-D sequences. To overcome this limitation,…
3D Instance Segmentation