arXiv AI
RoboShape: Information-Theoretic Point Cloud Representations for Privacy-Aware Robot Perception
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arXiv Computer Vision
Sep 18
Depth-Only Open-Vocabulary 3D Semantic Segmentation For Privacy-Preserving Robotic 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 Computer Vision
Sep 23
VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understanding
arXiv:2605.25059v4 Announce Type: replace Abstract: Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Em...
arXiv Computer Vision
Sep 24
Privacy-Preserving Semantic Segmentation from High-Resolution Depth and Ultra-Low-Resolution RGB
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 AI
Sep 1
Polis: 3D Self-Supervision at City Scale
arXiv:2608.29426v1 Announce Type: cross Abstract: Reliable semantic representations derived from city-scale 3D models are increasingly important for urban analysis, infrastructure monitoring, autonom...
arXiv Computer Vision
Aug 26
O3N: Omnidirectional Open-Vocabulary Occupancy Prediction for Embodied Intelligent Robotics
O3N is a novel framework that performs open‑vocabulary occupancy prediction from a single omnidirectional RGB image. It introduces a polar‑spiral voxel embedding (PsM) for continuous 360° spatial representation, an Occupancy Cost Aggregation (OCA) module that unifies geometric and semantic supervision, and a Natural Modality Alignment (NMA) pathway that aligns visual, voxel, and text features. Experiments show state‑of‑the‑art results on QuadOcc and Human360Occ benchmarks, with strong cross‑scene generalization and semantic scalability.
arXiv AI
Sep 1
Defending Wearable VLMs Against Private Attribute Inference
arXiv:2608.28691v1 Announce Type: cross Abstract: Wearable VLM pipelines promise continuous multimodal assistance from egocentric visual capture: a user asks a task-driven question about the surround...