13 citations · 37 across the 9 of their papers we have counts for
9 papers
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction
Minghan Zhu, Shizhong Han, Hong Cai +3
In this paper, we develop rotation-equivariant neural networks for 4D panoptic segmentation. 4D panoptic segmentation is a benchmark task for autonomous driving that requires recog…
MonoEdge: Monocular 3D Object Detection Using Local Perspectives
Minghan Zhu, Lingting Ge, Panqu Wang +1
We propose a novel approach for monocular 3D object detection by leveraging local perspective effects of each object. While the global perspective effect shown as size and position…
E2PN: Efficient SE(3)-Equivariant Point Network
Minghan Zhu, Maani Ghaffari, William A. Clark +1
This paper proposes a convolution structure for learning SE(3)-equivariant features from 3D point clouds. It can be viewed as an equivariant version of kernel point convolutions (K…
Correspondence-Free Point Cloud Registration with SO(3)-Equivariant Implicit Shape Representations
Minghan Zhu, Maani Ghaffari, Huei Peng
This paper proposes a correspondence-free method for point cloud rotational registration. We learn an embedding for each point cloud in a feature space that preserves the SO(3)-equ…
VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars
Yuanxin Zhong, Minghan Zhu, Huei Peng
A unified neural network structure is presented for joint 3D object detection and point cloud segmentation in this paper. We leverage rich supervision from both detection and segme…
Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography
Minghan Zhu, Songan Zhang, Yuanxin Zhong +3
This paper proposes a method to extract the position and pose of vehicles in the 3D world from a single traffic camera. Most previous monocular 3D vehicle detection algorithms focu…
Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss
Yuanxin Zhong, Minghan Zhu, Huei Peng
Object detection and tracking is a key task in autonomy. Specifically, 3D object detection and tracking have been an emerging hot topic recently. Although various methods have been…
Monocular Depth Prediction through Continuous 3D Loss
Minghan Zhu, Maani Ghaffari, Yuanxin Zhong +4
This paper reports a new continuous 3D loss function for learning depth from monocular images. The dense depth prediction from a monocular image is supervised using sparse LIDAR po…
Mcity Data Collection for Automated Vehicles Study
Yiqun Dong, Yuanxin Zhong, Wenbo Yu +5
The main goal of this paper is to introduce the data collection effort at Mcity targeting automated vehicle development. We captured a comprehensive set of data from a set of perce…