233 citations · 375 across the 11 of their papers we have counts for
21 papers · 1 filter
Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint in a Driving Scene
Tai-Yu Pan, Sooyoung Jeon, Mengdi Fan +6
Self-driving cars relying solely on ego-centric perception face limitations in sensing, often failing to detect occluded, faraway objects. Collaborative autonomous driving (CAV) se…
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…
Learning 3D Perception from Others' Predictions
Jinsu Yoo, Zhenyang Feng, Tai-Yu Pan +7
Accurate 3D object detection in real-world environments requires a huge amount of annotated data with high quality. Acquiring such data is tedious and expensive, and often needs re…
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…
Better Monocular 3D Detectors with LiDAR from the Past
Yurong You, Cheng Perng Phoo, Carlos Andres Diaz-Ruiz +5
Accurate 3D object detection is crucial to autonomous driving. Though LiDAR-based detectors have achieved impressive performance, the high cost of LiDAR sensors precludes their wid…
Pre-Training LiDAR-Based 3D Object Detectors Through Colorization
Tai-Yu Pan, Chenyang Ma, Tianle Chen +7
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we in…