26 citations · 31 across the 6 of their papers we have counts for
6 papers
LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
Anqi Joyce Yang, Sergio Casas, Nikita Dvornik +5
A major bottleneck to scaling-up training of self-driving perception systems are the human annotations required for supervision. A promising alternative is to leverage "auto-labell…
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss
Anas Mahmoud, Jordan S. K. Hu, Tianshu Kuai +3
An effective framework for learning 3D representations for perception tasks is distilling rich self-supervised image features via contrastive learning. However, image-to point repr…
Estimating Regression Predictive Distributions with Sample Networks
Ali Harakeh, Jordan Hu, Naiqing Guan +2
Estimating the uncertainty in deep neural network predictions is crucial for many real-world applications. A common approach to model uncertainty is to choose a parametric distribu…
Point Density-Aware Voxels for LiDAR 3D Object Detection
Jordan S. K. Hu, Tianshu Kuai, Steven L. Waslander
LiDAR has become one of the primary 3D object detection sensors in autonomous driving. However, LiDAR's diverging point pattern with increasing distance results in a non-uniform sa…
Dense Voxel Fusion for 3D Object Detection
Anas Mahmoud, Jordan S. K. Hu, Steven L. Waslander
Camera and LiDAR sensor modalities provide complementary appearance and geometric information useful for detecting 3D objects for autonomous vehicle applications. However, current…
Pattern-Aware Data Augmentation for LiDAR 3D Object Detection
Jordan S. K. Hu, Steven L. Waslander
Autonomous driving datasets are often skewed and in particular, lack training data for objects at farther distances from the ego vehicle. The imbalance of data causes a performance…