1 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
On the Feasibility and Opportunity of Autoregressive 3D Object Detection
Zanming Huang, Jinsu Yoo, Sooyoung Jeon +6
LiDAR-based 3D object detectors typically rely on proposal heads with hand-crafted components like anchor assignment and non-maximum suppression (NMS), complicating training and li…
When the City Teaches the Car: Label-Free 3D Perception from Infrastructure
Zhen Xu, Jinsu Yoo, Cristian Bautista +7
Building robust 3D perception for self-driving still relies heavily on large-scale data collection and manual annotation, yet this paradigm becomes impractical as deployment expand…
Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration
Katie Z Luo, Minh-Quan Dao, Zhenzhen Liu +9
Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X data…
DiffuBox: Refining 3D Object Detection with Point Diffusion
Xiangyu Chen, Zhenzhen Liu, Katie Z Luo +10
Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving. Recent models, however, face difficulties in maintaining h…
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
Katie Z Luo, Zhenzhen Liu, Xiangyu Chen +7
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Alt…