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cs.CV2026
Any to Full: Prompting Depth Anything for Depth Completion in One Stage
Zhiyuan Zhou, Ruofeng Liu, Taichi Liu +4
Accurate, dense depth estimation is crucial for robotic perception, but commodity sensors often yield sparse or incomplete measurements due to hardware limitations. Existing RGBD-f…
cs.CV2025
Towards 3D Objectness Learning in an Open World
Taichi Liu, Zhenyu Wang, Ruofeng Liu +2
Recent advancements in 3D object detection and novel category detection have made significant progress, yet research on learning generalized 3D objectness remains insufficient. In…
cs.CV2024★ 1 cited
OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation
Zhenyu Wang, Yali Li, Taichi Liu +2
In the current state of 3D object detection research, the severe scarcity of annotated 3D data, substantial disparities across different data modalities, and the absence of a unifi…