226 citations · 300 across the 19 of their papers we have counts for
19 papers
Point-DETR3D: Leveraging Imagery Data with Spatial Point Prior for Weakly Semi-supervised 3D Object Detection
Hongzhi Gao, Zheng Chen, Zehui Chen +4
Training high-accuracy 3D detectors necessitates massive labeled 3D annotations with 7 degree-of-freedom, which is laborious and time-consuming. Therefore, the form of point annota…
Stream Query Denoising for Vectorized HD Map Construction
Shuo Wang, Fan Jia, Yingfei Liu +6
To enhance perception performance in complex and extensive scenarios within the realm of autonomous driving, there has been a noteworthy focus on temporal modeling, with a particul…
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step
Zehui Chen, Weihua Du, Wenwei Zhang +8
Large language models (LLM) have achieved remarkable performance on various NLP tasks and are augmented by tools for broader applications. Yet, how to evaluate and analyze the tool…
Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation
Jiayi Ni, Senqiao Yang, Ran Xu +6
Since autonomous driving systems usually face dynamic and ever-changing environments, continual test-time adaptation (CTTA) has been proposed as a strategy for transferring deploye…
Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View
Shuo Wang, Xinhai Zhao, Hai-Ming Xu +5
Multi-view 3D object detection (MV3D-Det) in Bird-Eye-View (BEV) has drawn extensive attention due to its low cost and high efficiency. Although new algorithms for camera-only 3D o…
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…
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…
DETRDistill: A Universal Knowledge Distillation Framework for DETR-families
Jiahao Chang, Shuo Wang, Haiming Xu +3
Transformer-based detectors (DETRs) are becoming popular for their simple framework, but the large model size and heavy time consumption hinder their deployment in the real world.…
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices
Zhenyu Li, Zehui Chen, Jialei Xu +2
Monocular depth estimation is an essential task in the computer vision community. While tremendous successful methods have obtained excellent results, most of them are computationa…
AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection
Zehui Chen, Zhenyu Li, Shiquan Zhang +3
Point clouds and RGB images are two general perceptional sources in autonomous driving. The former can provide accurate localization of objects, and the latter is denser and richer…
Towards Model Generalization for Monocular 3D Object Detection
Zhenyu Li, Zehui Chen, Ang Li +4
Monocular 3D object detection (Mono3D) has achieved tremendous improvements with emerging large-scale autonomous driving datasets and the rapid development of deep learning techniq…
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,…
Graph-DETR3D: Rethinking Overlapping Regions 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. Due to its low cost and high efficiency, multi-view 3D object de…
DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation
Zhenyu Li, Zehui Chen, Xianming Liu +1
This paper aims to address the problem of supervised monocular depth estimation. We start with a meticulous pilot study to demonstrate that the long-range correlation is essential…
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…
AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection
Zehui Chen, Zhenyu Li, Shiquan Zhang +5
Object detection through either RGB images or the LiDAR point clouds has been extensively explored in autonomous driving. However, it remains challenging to make these two data sou…
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.…
Towards Fine-grained Large Object Segmentation 1st Place Solution to 3D AI Challenge 2020 -- Instance Segmentation Track
Zehui Chen, Qiaofei Li, Feng Zhao
This technical report introduces our solutions of Team 'FineGrainedSeg' for Instance Segmentation track in 3D AI Challenge 2020. In order to handle extremely large objects in 3D-FU…
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…