arXiv Computer Vision
MobileOcc: A Human-Aware Semantic Occupancy Dataset for Mobile Robots
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arXiv Computer Vision
1d ago
Out-of-Distribution Semantic Occupancy Prediction
arXiv:2506.21185v3 Announce Type: replace Abstract: 3D semantic occupancy prediction is crucial for autonomous driving, providing a dense, semantically rich environmental representation. However, exi...
arXiv Computer Vision
Sep 1
Geometry-Grounded Unified 3D Perception for Autonomous Driving
arXiv:2608.13147v2 Announce Type: replace Abstract: Camera-based autonomous driving perception requires a shared representation that preserves metric 3D structure across synchronized multi-camera str...
arXiv Computer Vision
Aug 31
iOSPointMapper: RealTime Pedestrian and Accessibility Mapping with Mobile AI
iOSPointMapper is a mobile app that performs real‑time, privacy‑conscious sidewalk mapping using on‑device semantic segmentation, LiDAR depth estimation, and fused GPS/IMU data on recent iPhones and iPads. It detects and localizes sidewalk‑relevant features such as traffic signs, traffic lights, and poles, and includes a user‑guided annotation interface for validating outputs before submission. The anonymized data is transmitted to the Transportation Data Exchange Initiative (TDEI), where it integrates with broader multimodal transportation datasets, and evaluations show the app’s potential for enhanced pedestrian mapping.
arXiv AI
Jul 28
Semantic Semi-Incremental Data-Association-Free Object SLAM
arXiv:2607. 23384v1 Announce Type: cross Abstract: Data association between landmark measurements and landmark variables has long been a central challenge in SLAM, as estimation accuracy depends critically on associating measurements with the correct landmark variables.
arXiv Machine Learning
Jun 8
Does Appearance Help? A Systematic Study of Image-Based Re-Identification in Online 3D Multi-Pedestrian Tracking
arXiv:2606. 07233v1 Announce Type: cross Abstract: LiDAR-based 3D Multi-Object Tracking (MOT) typically relies solely on geometric information, which is often insufficient to distinguish between targets during prolonged occlusions or in crowded human-populated environments.
Hugging Face Trending Papers
Jun 22
Humanoid-OmniOcc: Stereo-Based Full-View Occupancy Dataset for Embodied AI
Occupancy prediction at voxel-level granularity is essential for safe robotic navigation and interaction in complex environments. Existing occupancy datasets, however, are predominantly designed for autonomous driving with vehicle-centric biases -- forward-facing cameras, far-field geometry, and static road priors -- limiting their applicability to embodied humanoid perception.