Zobrazeno 1 - 10
of 22 753
pro vyhledávání: '"Dynamic objects."'
Visual-inertial odometry (VIO) is widely used in various fields, such as robots, drones, and autonomous vehicles, due to its low cost and complementary sensors. Most VIO methods presuppose that observed objects are static and time-invariant. However,
Externí odkaz:
http://arxiv.org/abs/2411.19289
Reconstructing objects and extracting high-quality surfaces play a vital role in the real world. Current 4D representations show the ability to render high-quality novel views for dynamic objects but cannot reconstruct high-quality meshes due to thei
Externí odkaz:
http://arxiv.org/abs/2409.14072
In this paper, we propose an algorithm to generate a static point cloud map based on LiDAR point cloud data. Our proposed pipeline detects dynamic objects using 3D object detectors and projects points of dynamic objects onto the ground. Typically, po
Externí odkaz:
http://arxiv.org/abs/2407.01073
In the process of urban environment mapping, the sequential accumulations of dynamic objects will leave a large number of traces in the map. These traces will usually have bad influences on the localization accuracy and navigation performance of the
Externí odkaz:
http://arxiv.org/abs/2406.15774
Autor:
Zhang, Haiying1 (AUTHOR) zhyhyds@outlook.com, Li, Zhengyang2 (AUTHOR) zyli@niaot.ac.cn, Wang, Chunyan1 (AUTHOR) wcy@cust.edu.cn
Publikováno v:
Sensors (14248220). Dec2024, Vol. 24 Issue 23, p7684. 17p.
Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments, partly due to the lack of large-scale labeled ground-truth annotations for learning occlusion. In this work, we introduce a novel framework
Externí odkaz:
http://arxiv.org/abs/2403.19022
The complex dynamicity of open-world objects presents non-negligible challenges for multi-object tracking (MOT), often manifested as severe deformations, fast motion, and occlusions. Most methods that solely depend on coarse-grained object cues, such
Externí odkaz:
http://arxiv.org/abs/2403.11186
This paper presents a novel method for the reconstruction of high-resolution temporal images in dynamic tomographic imaging, particularly for discrete objects with smooth boundaries that vary over time. Addressing the challenge of limited measurement
Externí odkaz:
http://arxiv.org/abs/2311.05269
Publikováno v:
AAAI 2024
Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network architectures, which allow for capturing implicit time-dependent information
Externí odkaz:
http://arxiv.org/abs/2401.13157
Self-supervised monocular depth estimation (DE) is an approach to learning depth without costly depth ground truths. However, it often struggles with moving objects that violate the static scene assumption during training. To address this issue, we i
Externí odkaz:
http://arxiv.org/abs/2312.10118