4 papers
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…
PARD-2: Target-Aligned Parallel Draft Model for Dual-Mode Speculative Decoding
Zihao An, Taichi Liu, Ziqiong Liu +3
Speculative decoding accelerates Large Language Models (LLMs) inference by using a lightweight draft model to propose candidate tokens that are verified in parallel by the target m…
Collaborate sim and real: Robot Bin Packing Learning in Real-world and Physical Engine
Lidi Zhang, Han Wu, Liyu Zhang +6
The 3D bin packing problem, with its diverse industrial applications, has garnered significant research attention in recent years. Existing approaches typically model it as a discr…
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…