6 papers · 1 filter
CorrelationFlow: A Training-Free Geometric Approach for LiDAR Scene Flow Estimation
Minh-Quan Dao, Yancong Lin, Julie Stephany Berrio Perez +2
LiDAR scene flow is the task of estimating point-wise motion from sequential point clouds. It is essential for autonomous driving, as it provides critical information about moving…
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…
Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor
Seung-Hyun Song, Dong-Hee Paek, Minh-Quan Dao +2
Recent advances in automotive four-dimensional (4D) Radar have enabled access to raw 4D Radar Tensor (4DRT), offering richer spatial and Doppler information than conventional point…
Label-Efficient 3D Object Detection For Road-Side Units
Minh-Quan Dao, Holger Caesar, Julie Stephany Berrio +4
Occlusion presents a significant challenge for safety-critical applications such as autonomous driving. Collaborative perception has recently attracted a large research interest th…
Aligning Bird-Eye View Representation of Point Cloud Sequences using Scene Flow
Minh-Quan Dao, Vincent Frémont, Elwan Héry
Low-resolution point clouds are challenging for object detection methods due to their sparsity. Densifying the present point cloud by concatenating it with its predecessors is a po…
3D-FlowNet: Event-based optical flow estimation with 3D representation
Haixin Sun, Minh-Quan Dao, Vincent Fremont
Event-based cameras can overpass frame-based cameras limitations for important tasks such as high-speed motion detection during self-driving cars navigation in low illumination con…