arXiv Computer Vision
MobileOcc: A Human-Aware Semantic Occupancy Dataset for Mobile Robots
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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
Oct 5
CrowdOcc: Monocular Semantic Scene Completion for Quadruped Robots in Crowded Indoor Environments
CrowdOcc introduces a new RGB‑D dataset and monocular semantic scene completion framework tailored for quadruped robots navigating crowded indoor spaces. The dataset comprises 25.1K frames across 11 scenes, with annotations derived via static‑dynamic decoupling. The framework employs Normal Guided Scene Geometry Fusion and Human‑Centric Sparse Interaction to robustly model occluded geometry and human‑scene relationships, achieving state‑of‑the‑art performance on the dataset’s test set.
arXiv Computer Vision
4d ago
CoGaussian: Open-Vocabulary Gaussian-based Collaborative 3D Scene Understanding
arXiv:2610.28530v1 Announce Type: new Abstract: Collaborative perception enables agents to retrieve the scene semantics and geometry beyond the field of view of any individual agent. This broader sce...
arXiv Computer Vision
2d ago
Foundation4D: Towards a unified perception and reconstruction model for autonomous vehicles
Foundation4D is a unified feed‑forward model that performs geometry estimation, 3D detection, semantic occupancy, and Gaussian reconstruction from multi‑camera driving video. It uses a shared backbone with factorized spatiotemporal attention to merge observations across cameras and time, and a video inference pipeline that refines camera trajectories via pose graph optimization and assembles object tracks. The authors also provide a data annotation pipeline and release the PAI‑Foundation4D benchmark, achieving state‑of‑the‑art results on depth, detection, and occupancy metrics and demonstrating that co‑training detection with geometry and occupancy improves performance.
arXiv Machine Learning
Sep 10
MamMA: A Mamba-Based Pedestrian Trajectory Prediction Algorithm Considering Occupancy Map and Pedestrian Awareness States
MamMA is a pedestrian trajectory prediction algorithm that leverages LiDAR-generated occupancy maps and egocentric vision sensor data. It partitions the occupancy map into patches to extract obstacle features and incorporates pedestrian awareness states, which influence perception and speed. Using a Mamba-based model, MamMA predicts future trajectories and outperforms state‑of‑the‑art methods on multiple benchmark datasets.
arXiv Computer Vision
6d ago
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."