Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Cao, Zidong"'
Recently, Depth Anything Model (DAM) - a type of depth foundation model - reveals impressive zero-shot capacity for diverse perspective images. Despite its success, it remains an open question regarding DAM's performance on 360 images that enjoy a la
Externí odkaz:
http://arxiv.org/abs/2406.13378
Autor:
Cao, Zidong, Wang, Lin
Monocular 360 depth estimation is challenging due to the inherent distortion of the equirectangular projection (ERP). This distortion causes a problem: spherical adjacent points are separated after being projected to the ERP plane, particularly in th
Externí odkaz:
http://arxiv.org/abs/2405.11564
Viewing omnidirectional images (ODIs) in virtual reality (VR) represents a novel form of media that provides immersive experiences for users to navigate and interact with digital content. Nonetheless, this sense of immersion can be greatly compromise
Externí odkaz:
http://arxiv.org/abs/2405.00351
Autor:
Ai, Hao, Cao, Zidong, Lu, Haonan, Chen, Chen, Ma, Jian, Zhou, Pengyuan, Kim, Tae-Kyun, Hui, Pan, Wang, Lin
360 images, with a field-of-view (FoV) of 180x360, provide immersive and realistic environments for emerging virtual reality (VR) applications, such as virtual tourism, where users desire to create diverse panoramic scenes from a narrow FoV photo the
Externí odkaz:
http://arxiv.org/abs/2401.10564
In this paper, we introduce Hi-Map, a novel monocular dense mapping approach based on Neural Radiance Field (NeRF). Hi-Map is exceptional in its capacity to achieve efficient and high-fidelity mapping using only posed RGB inputs. Our method eliminate
Externí odkaz:
http://arxiv.org/abs/2401.03203
Monocular depth estimation is a crucial task to measure distance relative to a camera, which is important for applications, such as robot navigation and self-driving. Traditional frame-based methods suffer from performance drops due to the limited dy
Externí odkaz:
http://arxiv.org/abs/2309.12842
Omnidirectional images (ODIs) have become increasingly popular, as their large field-of-view (FoV) can offer viewers the chance to freely choose the view directions in immersive environments such as virtual reality. The M\"obius transformation is typ
Externí odkaz:
http://arxiv.org/abs/2308.08114
In this paper, we introduce FMapping, an efficient neural field mapping framework that facilitates the continuous estimation of a colorized point cloud map in real-time dense RGB SLAM. To achieve this challenging goal without depth, a hurdle is how t
Externí odkaz:
http://arxiv.org/abs/2306.00579
To predict high-resolution (HR) omnidirectional depth map, existing methods typically leverage HR omnidirectional image (ODI) as the input via fully-supervised learning. However, in practice, taking HR ODI as input is undesired due to resource-constr
Externí odkaz:
http://arxiv.org/abs/2304.07967
The ability of scene understanding has sparked active research for panoramic image semantic segmentation. However, the performance is hampered by distortion of the equirectangular projection (ERP) and a lack of pixel-wise annotations. For this reason
Externí odkaz:
http://arxiv.org/abs/2303.14360