9 citations · 10 across the 5 of their papers we have counts for
5 papers
3D Open-Vocabulary Panoptic Segmentation with 2D-3D Vision-Language Distillation
Zihao Xiao, Longlong Jing, Shangxuan Wu +9
3D panoptic segmentation is a challenging perception task, especially in autonomous driving. It aims to predict both semantic and instance annotations for 3D points in a scene. Alt…
Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving
Mahyar Najibi, Jingwei Ji, Yin Zhou +4
Closed-set 3D perception models trained on only a pre-defined set of object categories can be inadequate for safety critical applications such as autonomous driving where new objec…
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences
Yingwei Li, Charles R. Qi, Yin Zhou +2
Occluded and long-range objects are ubiquitous and challenging for 3D object detection. Point cloud sequence data provide unique opportunities to improve such cases, as an occluded…
MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion
Chiyu Max Jiang, Andre Cornman, Cheolho Park +3
We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents. Such representation has several key advantages:…
Revisiting 3D Object Detection From an Egocentric Perspective
Boyang Deng, Charles R. Qi, Mahyar Najibi +3
3D object detection is a key module for safety-critical robotics applications such as autonomous driving. For these applications, we care most about how the detections affect the e…