DeepChoice: Learning ViewWeighting for Image-Guided 3D Semantic Segmentation

D2M-Net: decoupled dual matching for few-shot 3D point-cloud semantic segmentation

MuViSeg: Multi-View Segment Correspondences from Dense Geometry Priors

This work proposes three learned matching heads: a LightGlue-style attention head with DoubleSoftmax scoring on frozen MASt3R descriptors; a DPT-style multi-scale fusion module that exposes layered spatial detail from the VGGT foundation model before pooling; and a multi-view extension that performs joint self-attention over segments drawn from several views at once, recovering transitive correspondences that strictly pairwise matchers cannot reach.

Boundary Cues for Improved 3D Semantic Segmentation

A lightweight boundary-aware learning framework that explicitly models boundary regions during training is proposed, showing that incorporating boundary-aware supervision provides an effective and efficient approach to improving segmentation quality in challenging regions.

Stream3Dv2: Geometric-Semantic Fusion Enhanced Streaming Zero-Shot 3D Scene Understanding

Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supervised methods. However, deploying these models in real-world scenarios is severely hindered by their inability to efficiently handle streaming RGB-D inputs and their inherent vulnerability to noise 2D segmentation masks. To address these critical limitations, we propose Stream3Dv2, a novel training-free framework designed for robust streaming 3D perception. Stream3Dv2 processes sequential data through an original nested local-to-historical architecture, capturing multi-view consistency while circumventing the high computational overhead so as to support timely responses. At its core, we introduce a comprehensive geometric-semantic fusion mechanism that resolves geometric noise and semantic ambiguity by explicitly utilizing semantic guidance and formulating 3D segmentation as solving point-and-set merging and partitioning problems. Furthermore, we present an innovative manifold-distance-based point cloud refinement strategy. This approach leverages local manifold graphs for point-to-manifold optimization that mitigates the boundary delineation failures caused by Euclidean-distance metrics, and employs geometric bounding boxes to dynamically activate and update historical instances for achieving rapid manifold-to-manifold refinement. Extensive experiments on public datasets demonstrate that Stream3Dv2 consistently outperforms existing baselines in foundational open-vocabulary streaming 3D segmentation and detection. Finally, we show that integrating our framework with an LLM-based agent enables advanced language-driven 3D scene understanding, underscoring its potential for open-world embodied intelligence. Code will be updated at https://github.com/SubmissionsIn/Stream3D.

PointPDF V2: A Unified Framework for Continual Open-World 3D Semantic Segmentation.

This work proposes PointPDF V2, a unified framework that integrates open-set recognition (OSS) and incremental learning (IL) into a cohesive pipeline and introduces a more challenging continual OWSS protocol in 3D, where models must simultaneously preserve the known-class performance, acquire new knowledge, and still identify the remaining unknowns across sequential updates.

LiDAR Point Cloud Oversegmentation via SAM-based Knowledge Distillation

A SAM-guided framework for point cloud oversegmentation that significantly improves boundary recall and maintains high oracle accuracy while maintaining high oracle accuracy, and generalizes well to unseen datasets without retraining, showing strong cross-dataset inference capability.