activity
20202022
most citedBEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection

28 citations · 38 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV202228 cited

BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection

Zehui Chen, Zhenyu Li, Shiquan Zhang +3

3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding. Owing to its low cost and high efficiency, multi-view 3D object…

cs.CV2022

Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report

Andrey Ignatov, Grigory Malivenko, Radu Timofte +36

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus…

cs.CV20221 cited

Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training

Zhenyu Li, Zehui Chen, Ang Li +4

Monocular 3D object detection (Mono3D) has achieved unprecedented success with the advent of deep learning techniques and emerging large-scale autonomous driving datasets. However,…

cs.CV2022

SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations

Zhenyu Li, Zehui Chen, Ang Li +6

Pre-training has become a standard paradigm in many computer vision tasks. However, most of the methods are generally designed on the RGB image domain. Due to the discrepancy betwe…

cs.CV20212 cited

Disentangle Your Dense Object Detector

Zehui Chen, Chenhongyi Yang, Qiaofei Li +3

Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding.…

cs.CV20207 cited

1st Place Solutions of Waymo Open Dataset Challenge 2020 -- 2D Object Detection Track

Zehao Huang, Zehui Chen, Qiaofei Li +2

In this technical report, we present our solutions of Waymo Open Dataset (WOD) Challenge 2020 - 2D Object Track. We adopt FPN as our basic framework. Cascade RCNN, stacked PAFPN Ne…