572 citations · 1.3k across the 13 of their papers we have counts for
10 papers · 1 filter
STELLAR: Scaling 3D Perception Large Models for Autonomous Driving
Yingwei Li, Xin Huang, Yang Liu +13
Model scaling has demonstrated remarkable success through large-scale training on diverse datasets. It remains an open question whether the same paradigm would apply to autonomous…
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…
Instance Segmentation with Cross-Modal Consistency
Alex Zihao Zhu, Vincent Casser, Reza Mahjourian +2
Segmenting object instances is a key task in machine perception, with safety-critical applications in robotics and autonomous driving. We introduce a novel approach to instance seg…
Waymo Open Dataset: Panoramic Video Panoptic Segmentation
Jieru Mei, Alex Zihao Zhu, Xinchen Yan +6
Panoptic image segmentation is the computer vision task of finding groups of pixels in an image and assigning semantic classes and object instance identifiers to them. Research in…
EventGAN: Leveraging Large Scale Image Datasets for Event Cameras
Alex Zihao Zhu, Ziyun Wang, Kaung Khant +1
Event cameras provide a number of benefits over traditional cameras, such as the ability to track incredibly fast motions, high dynamic range, and low power consumption. However, t…
Motion Equivariant Networks for Event Cameras with the Temporal Normalization Transform
Alex Zihao Zhu, Ziyun Wang, Kostas Daniilidis
In this work, we propose a novel transformation for events from an event camera that is equivariant to optical flow under convolutions in the 3-D spatiotemporal domain. Events are…
Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion
Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney +1
In this work, we propose a novel framework for unsupervised learning for event cameras that learns motion information from only the event stream. In particular, we propose an input…
Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion
Alex Zihao Zhu, Wenxin Liu, Ziyun Wang +2
In this work, we propose a method that combines unsupervised deep learning predictions for optical flow and monocular disparity with a model based optimization procedure for instan…
Realtime Time Synchronized Event-based Stereo
Alex Zihao Zhu, Yibo Chen, Kostas Daniilidis
In this work, we propose a novel event based stereo method which addresses the problem of motion blur for a moving event camera. Our method uses the velocity of the camera and a ra…
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney +1
Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes. However, develop…