arXiv Computer Vision
CoGaussian: Open-Vocabulary Gaussian-based Collaborative 3D Scene Understanding
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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 Computer Vision
Sep 7
Out-of-Distribution Semantic Occupancy Prediction
The paper introduces Out-of-Distribution Semantic Occupancy Prediction, a task that focuses on detecting unknown objects in 3D voxel space for autonomous driving. It proposes Realistic Anomaly Augmentation to create two new datasets, VAA-KITTI and VAA-KITTI-360, and presents the OccOoD framework, which uses Cross‑Space Semantic Refinement to improve OoD detection while maintaining semantic occupancy accuracy. Experiments show OccOoD achieves an AuROC of 65.50% and an AuPRCr of 31.83% within a 1.2 m radius, demonstrating strong generalization to real‑world urban scenes.
arXiv Computer Vision
Sep 7
LetOccVote: Learning Weakly Supervised 3D Occupancy through Consensus
LetOccVote is a weakly supervised framework for 3D occupancy prediction that uses 2D pseudo-labels from vision foundation models. It introduces cross‑frame voting mechanisms—Depth Vote for geometric consistency and Semantic Vote for semantic reliability—to filter out erroneous pseudo-labels before training. The method achieves state‑of‑the‑art results on Occ3D‑nuScenes using only 2D supervision.
arXiv AI
Oct 7
OpenSplatGraph: From Dense Semantic Maps to Structured Scene Graphs for Open-Vocabulary Robot Perception
arXiv:2610.07569v1 Announce Type: cross Abstract: Dense 3D mapping with semantic understanding is essential for robotic perception in complex environments. Recent 3D Gaussian Splatting-based mapping...
arXiv Computer Vision
Sep 3
OccAnyScene: Towards Unified Indoor-Outdoor 3D Occupancy Prediction
OccAnyScene introduces a unified approach for 3D occupancy prediction across both indoor and outdoor scenes, addressing the challenge of handling diverse camera setups, spatial ranges, voxel specifications, and semantic taxonomies. The method builds on a pretrained depth model, using pixel-aligned frustum feature aggregation and frustum-parameterized Gaussian construction to generate scene-adaptive occupancy predictions. It achieves state-of-the-art performance, scoring 59.92% mIoU on Occ-ScanNet and 23.06% mIoU on SurroundOcc-nuScenes.
arXiv Computer Vision
Sep 2
MobileOcc: A Human-Aware Semantic Occupancy Dataset for Mobile Robots
arXiv:2511.16949v2 Announce Type: replace-cross Abstract: Dense 3D semantic occupancy perception is critical for mobile robots operating in pedestrian-rich environments, yet it remains underexplored...