From the 1 of 9 linked papers with an AI index.
9 papers
Map-Det3D: Metric Feed-Forward 3D Reconstruction Prior for Multi-view 3D Object Detection from Streaming Inputs
Yung-Hsu Yang, Luigi Piccinelli, Samuel Rota Bulò +7
Metric 3D object detection is a core capability for embodied agents, yet most reliable systems lean on depth sensors, trading away cost, power, and integration simplicity. This mot…
EgoTrack3D: A Modular Framework for Egocentric 3D Object Tracking
Jan Kulik, Bjarni Dagur Thor Karason, Yung-Hsu Yang +3
Understanding 3D scenes from egocentric video is fundamental for robotics and autonomous navigation, yet rapid viewpoint changes and partial occlusions make building structured rep…
DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving
Yung-Hsu Yang, Luigi Piccinelli, Siyuan Li +8
DVPSFormer is an online architecture that jointly estimates metric depth, semantic segmentation, and instance trajectories for autonomous driving by using explicit scene discretiza…
LIME: Learning Intent-aware Camera Motion from Egocentric Video
Boyang Sun, Jiajie Li, Yung-Hsu Yang +6
Autonomous robots often need to move their camera before they can act: to inspect an object, reveal an occluded region, or obtain a view that responds to a user's intent. While vis…
UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler
Luigi Piccinelli, Christos Sakaridis, Yung-Hsu Yang +4
Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However, the remarkable accuracy of recent MMDE methods is c…
3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection
Yung-Hsu Yang, Luigi Piccinelli, Mattia Segu +6
Monocular 3D object detection is valuable for various applications such as robotics and AR/VR. Existing methods are confined to closed-set settings, where the training and testing…