Zobrazeno 1 - 10
of 7 716
pro vyhledávání: '"3D perception"'
Unsupervised 3D representation learning via masked-and-reconstruction with differentiable rendering is promising to reduce the labeling burden for fusion 3D perception. However, previous literature conduct pre-training for different modalities separa
Externí odkaz:
http://arxiv.org/abs/2412.03059
In embodied intelligence systems, a key component is 3D perception algorithm, which enables agents to understand their surrounding environments. Previous algorithms primarily rely on point cloud, which, despite offering precise geometric information,
Externí odkaz:
http://arxiv.org/abs/2411.14869
Unmanned Aerial Vehicles (UAVs), equipped with cameras, are employed in numerous applications, including aerial photography, surveillance, and agriculture. In these applications, robust object detection and tracking are essential for the effective de
Externí odkaz:
http://arxiv.org/abs/2410.11125
With the development of AI-assisted driving, numerous methods have emerged for ego-vehicle 3D perception tasks, but there has been limited research on roadside perception. With its ability to provide a global view and a broader sensing range, the roa
Externí odkaz:
http://arxiv.org/abs/2410.15814
Autor:
Yoo, Jinsu, Feng, Zhenyang, Pan, Tai-Yu, Sun, Yihong, Phoo, Cheng Perng, Chen, Xiangyu, Campbell, Mark, Weinberger, Kilian Q., Hariharan, Bharath, Chao, Wei-Lun
Accurate 3D object detection in real-world environments requires a huge amount of annotated data with high quality. Acquiring such data is tedious and expensive, and often needs repeated effort when a new sensor is adopted or when the detector is dep
Externí odkaz:
http://arxiv.org/abs/2410.02646
While 3D object bounding box (bbox) representation has been widely used in autonomous driving perception, it lacks the ability to capture the precise details of an object's intrinsic geometry. Recently, occupancy has emerged as a promising alternativ
Externí odkaz:
http://arxiv.org/abs/2412.05154
This paper studies point cloud perception within outdoor environments. Existing methods face limitations in recognizing objects located at a distance or occluded, due to the sparse nature of outdoor point clouds. In this work, we observe a significan
Externí odkaz:
http://arxiv.org/abs/2411.07742
Autor:
Yang, Fan, Zhao, Sicheng, Zhang, Yanhao, Chen, Haoxiang, Chen, Hui, Tang, Wenbo, Lu, Haonan, Xu, Pengfei, Yang, Zhenyu, Han, Jungong, Ding, Guiguang
Recent advancements in autonomous driving, augmented reality, robotics, and embodied intelligence have necessitated 3D perception algorithms. However, current 3D perception methods, particularly small models, struggle with processing logical reasonin
Externí odkaz:
http://arxiv.org/abs/2408.07422
Concurrent processing of multiple autonomous driving 3D perception tasks within the same spatiotemporal scene poses a significant challenge, in particular due to the computational inefficiencies and feature competition between tasks when using tradit
Externí odkaz:
http://arxiv.org/abs/2407.10876
Perceiving the surrounding environment is a fundamental task in autonomous driving. To obtain highly accurate perception results, modern autonomous driving systems typically employ multi-modal sensors to collect comprehensive environmental data. Amon
Externí odkaz:
http://arxiv.org/abs/2409.04979