40 citations · 99 across the 9 of their papers we have counts for
9 papers
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…
Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field
Leheng Li, Qing Lian, Luozhou Wang +2
This work explores the use of 3D generative models to synthesize training data for 3D vision tasks. The key requirements of the generative models are that the generated data should…
MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones
Tai Wang, Qing Lian, Chenming Zhu +2
In this technical report, we present our solution, dubbed MV-FCOS3D++, for the Camera-Only 3D Detection track in Waymo Open Dataset Challenge 2022. For multi-view camera-only 3D de…
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection
Qing Lian, Peiliang Li, Xiaozhi Chen
Due to the inherent ill-posed nature of 2D-3D projection, monocular 3D object detection lacks accurate depth recovery ability. Although the deep neural network (DNN) enables monocu…
Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms
Yunqi Wang, Furui Liu, Zhitang Chen +4
Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve t…
Exploring Geometric Consistency for Monocular 3D Object Detection
Qing Lian, Botao Ye, Ruijia Xu +2
This paper investigates the geometric consistency for monocular 3D object detection, which suffers from the ill-posed depth estimation. We first conduct a thorough analysis to reve…
Weakly Supervised Disentangled Generative Causal Representation Learning
Xinwei Shen, Furui Liu, Hanze Dong +3
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information. Unlike existing disentanglement methods that en…
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
Qing Lian, Fengmao Lv, Lixin Duan +1
We propose a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic…
Known-class Aware Self-ensemble for Open Set Domain Adaptation
Qing Lian, Wen Li, Lin Chen +1
Existing domain adaptation methods generally assume different domains have the identical label space, which is quite restrict for real-world applications. In this paper, we focus o…