Zobrazeno 1 - 10
of 24 065
pro vyhledávání: '"Range image"'
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Point cloud segmentation (PCS) plays an essential role in robot perception and navigation tasks. To efficiently understand large-scale outdoor point clouds, their range image representation is commonly adopted. This image-like representation is compa
Externí odkaz:
http://arxiv.org/abs/2405.10175
Autor:
Li, Baiang, Ma, Sizhuo, Zeng, Yanhong, Xu, Xiaogang, Fang, Youqing, Zhang, Zhao, Wang, Jian, Chen, Kai
Capturing High Dynamic Range (HDR) scenery using 8-bit cameras often suffers from over-/underexposure, loss of fine details due to low bit-depth compression, skewed color distributions, and strong noise in dark areas. Traditional LDR image enhancemen
Externí odkaz:
http://arxiv.org/abs/2406.09389
For recent diffusion-based generative models, maintaining consistent content across a series of generated images, especially those containing subjects and complex details, presents a significant challenge. In this paper, we propose a new way of self-
Externí odkaz:
http://arxiv.org/abs/2405.01434
In the field of resource-constrained robots and the need for effective place recognition in multi-robotic systems, this article introduces RecNet, a novel approach that concurrently addresses both challenges. The core of RecNet's methodology involves
Externí odkaz:
http://arxiv.org/abs/2402.02192
High Dynamic Range (HDR) imaging aims to generate an artifact-free HDR image with realistic details by fusing multi-exposure Low Dynamic Range (LDR) images. Caused by large motion and severe under-/over-exposure among input LDR images, HDR imaging su
Externí odkaz:
http://arxiv.org/abs/2403.06831
Autor:
Velasco-Sánchez, Edison P., Muñoz-Bañón, Miguel Ángel, Candelas, Francisco A., Puente, Santiago T., Torres, Fernando
In unstructured outdoor environments, robotics requires accurate and efficient odometry with low computational time. Existing low-bias LiDAR odometry methods are often computationally expensive. To address this problem, we present a lightweight LiDAR
Externí odkaz:
http://arxiv.org/abs/2311.07291
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.