activity
20132023
most citedUnderstanding Convolution for Semantic Segmentation

47 citations · 147 across the 14 of their papers we have counts for

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Showing cs.CVShow all

11 papers · 1 filter

cs.CV2023

LiDARFormer: A Unified Transformer-based Multi-task Network for LiDAR Perception

Zixiang Zhou, Dongqiangzi Ye, Weijia Chen +4

There is a recent trend in the LiDAR perception field towards unifying multiple tasks in a single strong network with improved performance, as opposed to using separate networks fo…

cs.CV2023

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…

cs.CV2022★ 10 cited

CenterFormer: Center-based Transformer for 3D Object Detection

Zixiang Zhou, Xiangchen Zhao, Yu Wang +2

Query-based transformer has shown great potential in constructing long-range attention in many image-domain tasks, but has rarely been considered in LiDAR-based 3D object detection…

cs.CV2022★ 6 cited

LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception

Dongqiangzi Ye, Zixiang Zhou, Weijia Chen +4

LiDAR-based 3D object detection, semantic segmentation, and panoptic segmentation are usually implemented in specialized networks with distinctive architectures that are difficult…

cs.CV2022★ 7 cited

LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network

Dongqiangzi Ye, Weijia Chen, Zixiang Zhou +4

This technical report presents the 1st place winning solution for the Waymo Open Dataset 3D semantic segmentation challenge 2022. Our network, termed LidarMultiNet, unifies the maj…

cs.CV2021★ 9 cited

ES-Net: An Efficient Stereo Matching Network

Zhengyu Huang, Theodore B. Norris, Panqu Wang

Dense stereo matching with deep neural networks is of great interest to the research community. Existing stereo matching networks typically use slow and computationally expensive 3…