Zobrazeno 1 - 10
of 2 331
pro vyhledávání: '"Ren-Liu"'
Autor:
Chen, Kaiyuan, Hari, Kush, Chung, Trinity, Wang, Michael, Tian, Nan, Juette, Christian, Ichnowski, Jeffrey, Ren, Liu, Kubiatowicz, John, Stoica, Ion, Goldberg, Ken
Cloud robotics enables robots to offload complex computational tasks to cloud servers for performance and ease of management. However, cloud compute can be costly, cloud services can suffer occasional downtime, and connectivity between the robot and
Externí odkaz:
http://arxiv.org/abs/2412.05408
Bird's eye view (BEV)-based 3D perception plays a crucial role in autonomous driving applications. The rise of large language models has spurred interest in BEV-based captioning to understand object behavior in the surrounding environment. However, e
Externí odkaz:
http://arxiv.org/abs/2411.10639
Autor:
Chen, Kaiyuan, Tian, Nan, Juette, Christian, Qiu, Tianshuang, Ren, Liu, Kubiatowicz, John, Goldberg, Ken
Cloud robotics enables robots to offload computationally intensive tasks to cloud servers for performance, cost, and ease of management. However, the network and cloud computing infrastructure are not designed for reliable timing guarantees, due to f
Externí odkaz:
http://arxiv.org/abs/2410.05562
Autonomous driving in complex urban scenarios requires 3D perception to be both comprehensive and precise. Traditional 3D perception methods focus on object detection, resulting in sparse representations that lack environmental detail. Recent approac
Externí odkaz:
http://arxiv.org/abs/2408.13454
Autor:
Zhang, Hengyuan, Paz, David, Guo, Yuliang, Das, Arun, Huang, Xinyu, Haug, Karsten, Christensen, Henrik I., Ren, Liu
Autonomous driving for urban and highway driving applications often requires High Definition (HD) maps to generate a navigation plan. Nevertheless, various challenges arise when generating and maintaining HD maps at scale. While recent online mapping
Externí odkaz:
http://arxiv.org/abs/2408.01471
The emergence of large-scale pre-trained models has heightened their application in various downstream tasks, yet deployment is a challenge in environments with limited computational resources. Knowledge distillation has emerged as a solution in such
Externí odkaz:
http://arxiv.org/abs/2406.17838
Autor:
Wang, Xiaoqi, He, Wenbin, Xuan, Xiwei, Sebastian, Clint, Ono, Jorge Piazentin, Li, Xin, Behpour, Sima, Doan, Thang, Gou, Liang, Shen, Han Wei, Ren, Liu
The open-vocabulary image segmentation task involves partitioning images into semantically meaningful segments and classifying them with flexible text-defined categories. The recent vision-based foundation models such as the Segment Anything Model (S
Externí odkaz:
http://arxiv.org/abs/2406.05271
Despite demonstrating robust capabilities in performing tasks related to general-domain data-operation tasks, Large Language Models (LLMs) may exhibit shortcomings when applied to domain-specific tasks. We consider the design of domain-specific AI-po
Externí odkaz:
http://arxiv.org/abs/2405.05548
Autor:
Sun, Su, Zhao, Cheng, Guo, Yuliang, Wang, Ruoyu, Huang, Xinyu, Chen, Yingjie Victor, Ren, Liu
In this paper, we present a novel indoor 3D reconstruction method with occluded surface completion, given a sequence of depth readings. Prior state-of-the-art (SOTA) methods only focus on the reconstruction of the visible areas in a scene, neglecting
Externí odkaz:
http://arxiv.org/abs/2404.03070
Autor:
Zhao, Cheng, Sun, Su, Wang, Ruoyu, Guo, Yuliang, Wan, Jun-Jun, Huang, Zhou, Huang, Xinyu, Chen, Yingjie Victor, Ren, Liu
Most 3D Gaussian Splatting (3D-GS) based methods for urban scenes initialize 3D Gaussians directly with 3D LiDAR points, which not only underutilizes LiDAR data capabilities but also overlooks the potential advantages of fusing LiDAR with camera data
Externí odkaz:
http://arxiv.org/abs/2404.02410