Zobrazeno 1 - 10
of 234
pro vyhledávání: '"Liu, Youquan"'
Autor:
Sun, Jiahao, Qing, Chunmei, Xu, Xiang, Kong, Lingdong, Liu, Youquan, Li, Li, Zhu, Chenming, Zhang, Jingwei, Xiao, Zeqi, Chen, Runnan, Wang, Tai, Zhang, Wenwei, Chen, Kai
In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments. Traditional approaches often rely on disparate, standalone codebases, hindering unified advancements and fai
Externí odkaz:
http://arxiv.org/abs/2405.14870
Event-based semantic segmentation (ESS) is a fundamental yet challenging task for event camera sensing. The difficulties in interpreting and annotating event data limit its scalability. While domain adaptation from images to event data can help to mi
Externí odkaz:
http://arxiv.org/abs/2405.05259
Autor:
Liu, Youquan, Kong, Lingdong, Wu, Xiaoyang, Chen, Runnan, Li, Xin, Pan, Liang, Liu, Ziwei, Ma, Yuexin
A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net, a one-of-a-kind framework for fulfilling multi-task, multi-dataset, multi-moda
Externí odkaz:
http://arxiv.org/abs/2405.01538
Autor:
Xu, Jingyi, Yang, Weidong, Kong, Lingdong, Liu, Youquan, Zhang, Rui, Zhou, Qingyuan, Fei, Ben
Unsupervised domain adaptation (UDA) is vital for alleviating the workload of labeling 3D point cloud data and mitigating the absence of labels when facing a newly defined domain. Various methods of utilizing images to enhance the performance of cros
Externí odkaz:
http://arxiv.org/abs/2403.10001
Autor:
Peng, Xidong, Chen, Runnan, Qiao, Feng, Kong, Lingdong, Liu, Youquan, Sun, Yujing, Wang, Tai, Zhu, Xinge, Ma, Yuexin
Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data. Especially for LiDAR point clouds, the domain discrepancy becomes obvious acro
Externí odkaz:
http://arxiv.org/abs/2310.08820
Autor:
Liu, Youquan, Chen, Runnan, Li, Xin, Kong, Lingdong, Yang, Yuchen, Xia, Zhaoyang, Bai, Yeqi, Zhu, Xinge, Ma, Yuexin, Li, Yikang, Qiao, Yu, Hou, Yuenan
Point-, voxel-, and range-views are three representative forms of point clouds. All of them have accurate 3D measurements but lack color and texture information. RGB images are a natural complement to these point cloud views and fully utilizing the c
Externí odkaz:
http://arxiv.org/abs/2309.05573
Autor:
Liu, Youquan, Kong, Lingdong, Cen, Jun, Chen, Runnan, Zhang, Wenwei, Pan, Liang, Chen, Kai, Liu, Ziwei
Recent advancements in vision foundation models (VFMs) have opened up new possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a novel framework that harnesses VFMs for segmenting diverse automotive point clou
Externí odkaz:
http://arxiv.org/abs/2306.09347
Autor:
Chen, Runnan, Liu, Youquan, Kong, Lingdong, Chen, Nenglun, Zhu, Xinge, Ma, Yuexin, Liu, Tongliang, Wang, Wenping
Vision foundation models such as Contrastive Vision-Language Pre-training (CLIP) and Segment Anything (SAM) have demonstrated impressive zero-shot performance on image classification and segmentation tasks. However, the incorporation of CLIP and SAM
Externí odkaz:
http://arxiv.org/abs/2306.03899
Autor:
Kong, Lingdong, Liu, Youquan, Li, Xin, Chen, Runnan, Zhang, Wenwei, Ren, Jiawei, Pan, Liang, Chen, Kai, Liu, Ziwei
The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets often contain data that are meticulously cleaned. Such configurat
Externí odkaz:
http://arxiv.org/abs/2303.17597
Autor:
Xia, Zhaoyang, Liu, Youquan, Li, Xin, Zhu, Xinge, Ma, Yuexin, Li, Yikang, Hou, Yuenan, Qiao, Yu
Training deep models for semantic scene completion (SSC) is challenging due to the sparse and incomplete input, a large quantity of objects of diverse scales as well as the inherent label noise for moving objects. To address the above-mentioned probl
Externí odkaz:
http://arxiv.org/abs/2303.06884