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20202023
most citedCylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

34 citations · 53 across the 10 of their papers we have counts for

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11 papers · 1 filter

cs.CV2023★ 1 cited

Scene as Occupancy

Chonghao Sima, Wenwen Tong, Tai Wang +8

Human driver can easily describe the complex traffic scene by visual system. Such an ability of precise perception is essential for driver's planning. To achieve this, a geometry-a…

cs.CV2023★ 4 cited

DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking

Qing Lian, Tai Wang, Dahua Lin +1

Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation. However, they typically…

cs.CV2023★ 1 cited

MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training

Runsen Xu, Tai Wang, Wenwei Zhang +4

This paper introduces the Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training and a carefully designed data-efficient 3D object dete…

cs.CV2023

Position-Guided Point Cloud Panoptic Segmentation Transformer

Zeqi Xiao, Wenwei Zhang, Tai Wang +3

DEtection TRansformer (DETR) started a trend that uses a group of learnable queries for unified visual perception. This work begins by applying this appealing paradigm to LiDAR-bas…

cs.CV2023★ 1 cited

GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding

Jihao Liu, Tai Wang, Boxiao Liu +3

Multi-view camera-based 3D detection is a challenging problem in computer vision. Recent works leverage a pretrained LiDAR detection model to transfer knowledge to a camera-based s…

cs.CV2021★ 1 cited

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception

Xinge Zhu, Hui Zhou, Tai Wang +6

State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud semantic segmentation, panoptic segmentation and 3D detection, \etc) often project the poin…