85 citations · 211 across the 20 of their papers we have counts for
21 papers
NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors
Congyue Deng, Chiyu "Max'' Jiang, Charles R. Qi +4
2D-to-3D reconstruction is an ill-posed problem, yet humans are good at solving this problem due to their prior knowledge of the 3D world developed over years. Driven by this obser…
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations
Zhaoqi Leng, Guowang Li, Chenxi Liu +5
Data augmentations are important in training high-performance 3D object detectors for point clouds. Despite recent efforts on designing new data augmentations, perhaps surprisingly…
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds
Zhaoqi Leng, Shuyang Cheng, Benjamin Caine +5
Data augmentation is an important technique to improve data efficiency and save labeling cost for 3D detection in point clouds. Yet, existing augmentation policies have so far been…
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
Eli Bronstein, Mark Palatucci, Dominik Notz +14
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard MGAIL…
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection
Jyh-Jing Hwang, Henrik Kretzschmar, Joshua Manela +4
Robust 3D object detection is critical for safe autonomous driving. Camera and radar sensors are synergistic as they capture complementary information and work well under different…
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining
Chiyu Max Jiang, Mahyar Najibi, Charles R. Qi +2
Continued improvements in deep learning architectures have steadily advanced the overall performance of 3D object detectors to levels on par with humans for certain tasks and datas…