arXiv:2506. 11585v2 Announce Type: replace-cross Abstract: We introduce OV-MAP, a novel approach to open-world 3D mapping for mobile robots by integrating open-features into 3D maps to enhance object recognition capabilities.
arXiv:2607. 17778v1 Announce Type: cross Abstract: Class-agnostic 3D instance segmentation is critical for robotic systems operating in unknown environments, enabling perception of previously unseen objects for reliable manipulation and navigation.
Class-agnostic 3D instance segmentation is critical for robotic systems operating in unknown environments, enabling perception of previously unseen objects for reliable manipulation and navigation. Existing approaches typically project per-frame 2D instance masks into 3D and merge them, which often breaks object identities across time and yields fragmented 3D instances.
arXiv:2608.21136v1 Announce Type: new Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supe...
arXiv:2606. 08014v1 Announce Type: cross Abstract: Accurate 3D instance segmentation in point cloud data is critical for machine vision applications.
SAM‑V is a geometry‑aware extension of the Segment Anything Model (SAM) that integrates 3D priors from a feed‑forward geometry model (VGGT) into 2D segmentation. It uses a prompt‑fusion mechanism to combine sparse SAM prompts with view‑specific camera tokens and local VGGT features, enabling a mask decoder that attends to both dense 2D and 3D cues. The resulting end‑to‑end system produces consistent multi‑view instance segmentation in a single forward pass, achieving significant gains on the IGGT 3D tracking benchmark without offline mask matching or explicit 3D reconstruction.
SenseFuse introduces a label‑free fusion approach that balances 2D image and 3D shape encoders for open‑vocabulary 3D instance segmentation. By selecting a scene‑level fusion weight through an adaptive, sensitivity‑based mechanism, it improves mask labeling accuracy across multiple datasets, recovering up to 93% of the potential gain from an oracle weight. The method demonstrates that image and shape encoders have complementary failure patterns, leading to higher instance AP in most evaluated settings.
Lang3DSeg introduces a point‑transformer backbone for open‑vocabulary, annotation‑free 3D LiDAR segmentation, trained from scratch without geometric pre‑training. It tackles noise from 2D‑to‑3D label projections by applying a class‑priority rule and truncating projected instances at depth gaps, thereby correcting depth‑ambiguity errors. The method achieves state‑of‑the‑art results on nuScenes (52.8 % mIoU) and SemanticKITTI (41.4 % mIoU) while operating in real‑time on a single LiDAR sweep.
TRACKGRAPH is an online open‑vocabulary 3D mapping system that tracks 2D masks in the image stream before fusing them into a class‑agnostic 3D segment layer within a hierarchical scene graph. It uses FastSAM and CLIP for sparse keyframes, DINOv3 for dense mask propagation, and compact multi‑view CLIP embeddings for open‑vocabulary retrieval. The method outperforms state‑of‑the‑art mapping techniques on Replica, ScanNet++, and HM3D, achieving higher synonym frequency, faster processing, and lower GPU memory usage, and has been deployed on quadruped robots and drones at real‑time rates.
The paper introduces a privacy‑preserving approach for semantic segmentation that fuses high‑resolution depth with ultra‑low‑resolution RGB images. A joint 2D framework uses depth to guide RGB reconstruction and RGB‑D segmentation, while an end‑to‑end 2D‑to‑3D pipeline consolidates 2D features for 3D segmentation. Experiments on ScanNet demonstrate superior 2D and 3D performance compared to other privacy‑preserving methods, strong zero‑shot transfer to SUN RGB‑D and SceneNN, and reduced recoverability of sensitive data, with real‑robot tests showing effective object‑goal navigation.
arXiv:2509. 24528v4 Announce Type: replace-cross Abstract: Object retrieval from a scene has become a new trend of research due to its numerous applications.
The paper introduces a privacy‑preserving approach to open‑vocabulary 3D semantic segmentation that operates solely on depth data, eliminating the use of RGB images to avoid disclosing scene‑specific visual information. It proposes a stricter depth‑only evaluation protocol and presents UTTO, a model‑agnostic uncertainty‑guided test‑time optimization framework that refines predictions from frozen open‑vocabulary 3D backbones using structured predictive uncertainty. Experiments on ScanNet and Matterport3D show consistent improvements, and additional analyses demonstrate the method’s relevance for privacy‑constrained robotic applications.
arXiv:2510. 11014v2 Announce Type: replace-cross Abstract: Autonomous robots often view rooms only partially, through a doorway, where the walls and scene structure hide the geometry and task-relevant semantics needed for safe navigation and goal-directed action.