arXiv Computer Vision
MV2GF: Multi-view Pedestrian Detection with a Visual Geometric Foundation Model
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DVGT: Driving Visual Geometry Transformer
arXiv:2512. 16919v2 Announce Type: replace-cross Abstract: Perceiving and reconstructing 3D scene geometry from visual inputs is crucial for autonomous driving.
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Beyond 2D Matching: A Unified Single-Stage Framework for Geometry-Aware Cross-View Object Geo-Localization
arXiv:2606. 30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.
arXiv Computer Vision
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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 Computer Vision
Oct 15
MonoMIP: Multi-Height Inverse Perspective Mapping for Monocular 3D Object Detection
arXiv:2610.19071v1 Announce Type: new Abstract: Monocular 3D object detection methods increasingly rely on depth-guided transformers, but they operate entirely in the perspective image plane where sp...
arXiv Computer Vision
Sep 16
Multi-View Foundation Models
arXiv:2512.15708v2 Announce Type: replace Abstract: Foundation models are vital tools in various Computer Vision applications. They take as input a single RGB image and output a deep feature represen...
arXiv AI
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Cross-View Urban Traffic Dataset: Drone-Supervised Ground Truth for Monocular Bird's-Eye View Localization
arXiv:2606. 07708v1 Announce Type: cross Abstract: We introduce a dataset and benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections.