229 citations · 867 across the 17 of their papers we have counts for
17 papers
NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects
Dakshit Agrawal, Jiajie Xu, Siva Karthik Mustikovela +3
We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene. Compared to prior work, our framework s…
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting
Mao Ye, Gregory P. Meyer, Yuning Chai +1
Balancing efficiency and accuracy is a long-standing problem for deploying deep learning models. The trade-off is even more important for real-time safety-critical systems like aut…
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving
Reza Mahjourian, Jinkyu Kim, Yuning Chai +3
We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving. Our representation is a spatio-temporal g…
HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps
Lu Mi, Hang Zhao, Charlie Nash +7
High Definition (HD) maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. They are critical for several key stages in an autonomous drivin…
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels
Yuning Chai, Pei Sun, Jiquan Ngiam +5
3D object detection is vital for many robotics applications. For tasks where a 2D perspective range image exists, we propose to learn a 3D representation directly from this range i…
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
Pei Sun, Weiyue Wang, Yuning Chai +5
The detection of 3D objects from LiDAR data is a critical component in most autonomous driving systems. Safe, high speed driving needs larger detection ranges, which are enabled by…