arXiv Computer Vision
Spheriverse: 3D Scene Understanding from Spherical Observations in the Wild
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arXiv Computer Vision
Sep 30
SphMind: Towards Robust, Training-Free VLM-based Spatial Reasoning with a 360 Camera
arXiv:2609.33462v2 Announce Type: replace Abstract: Omnidirectional or 360 cameras provide embodied AI agents with a holistic, wide field-of-view (FoV) view of their surroundings, motivating the use...
arXiv Computer Vision
Oct 27
LegoOcc++: Towards Consistent Open-Vocabulary 3D Occupancy Prediction
arXiv:2610.34093v1 Announce Type: new Abstract: Open-vocabulary 3D occupancy prediction is important for embodied agents operating in complex environments whose semantic categories extend beyond fixe...
arXiv Computer Vision
Oct 19
PanoBench: A Panoptic Occupancy Prediction Benchmark for Autonomous Driving
PanoBench is a new camera‑centric benchmark for panoptic occupancy prediction that unifies 3D geometry, semantics, and instance identities across synthetic and real‑world driving scenes. It combines three datasets—PanoBench‑CarlaOcc, PanoBench‑nuScenes, and PanoBench‑KITTI360—through canonical interfaces and unified evaluation protocols, providing multi‑resolution annotations with persistent instance identities. The benchmark introduces a novel reconstruction pipeline for real‑world data, a unified valid‑mask protocol, and analyses of observation fidelity, geometric completeness, spatial continuity, and temporal consistency, while also benchmarking methods on semantic, panoptic, and 4D occupancy forecasting. whyItMatters":"PanoBench addresses the scarcity of instance‑aware annotations in panoptic occupancy research by offering comprehensive, high‑quality annotations and evaluation protocols that enable more robust development and comparison of autonomous driving perception methods."
arXiv AI
6d ago
GCSR-OCC: Geometry Calibration and Semantic Routing for Open-Vocabulary Occupancy Prediction
GCSR‑OCC introduces geometry calibration and semantic routing to improve open‑vocabulary 3D occupancy prediction from a single RGB image. It uses an Evidence‑Conserving Gaussian‑to‑Occupancy operator to mitigate scale inflation and a Multi‑Mode Query‑First Gaussian Semantic Routing module to align multiple semantic modes with text queries, refining voxel‑level predictions. The method achieves state‑of‑the‑art results on Occ‑ScanNet, improving geometric IoU to 63.31% and semantic mIoU to 27.44%, while reducing aggregation latency from 19.76 ms to 12.88 ms.
arXiv AI
Oct 5
OmniAct3D: Leveraging Foundation Geometry and Evidence-Grounded Reasoning for Panoramic 3D Detection
OmniAct3D is a framework that adapts Vision Foundation Model (VFM) detectors trained on perspective images to equirectangular projection (ERP) panoramic images for 3D detection. It introduces the ERP-Ray Geometry Adapter (ERGA-Ray) to model spherical rays, the Visual-Action Reasoning Chain (VARC) to ground hypotheses in panoramic evidence, and the Appearance-Guided Heading Expert (AGHE) to recover high‑resolution heading cues. The method achieves a 2.96 NDS improvement on Spheriverse and a 24.87 mAP gain on PanoMMOcc, while preserving most of the baseline’s performance across configurations.
arXiv Computer Vision
Oct 27
OVM-Occ: Open-Vocabulary Monocular Occupancy Prediction via Geometry-Aware Vision-Language Distillation
arXiv:2610.35312v1 Announce Type: new Abstract: Monocular occupancy prediction in indoor scenes aims to recover a scene's 3D geometry and semantics from a single RGB image. Existing methods are restr...