Zobrazeno 1 - 10
of 4 749
pro vyhledávání: '"3D scene reconstruction"'
Autor:
Zhou, Yiming, Zeng, Zixuan, Chen, Andi, Zhou, Xiaofan, Ni, Haowei, Zhang, Shiyao, Li, Panfeng, Liu, Liangxi, Zheng, Mengyao, Chen, Xupeng
Publikováno v:
Proceedings of the 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS), 2024, pp. 926-931
Exploring the capabilities of Neural Radiance Fields (NeRF) and Gaussian-based methods in the context of 3D scene reconstruction, this study contrasts these modern approaches with traditional Simultaneous Localization and Mapping (SLAM) systems. Util
Externí odkaz:
http://arxiv.org/abs/2408.04268
The process of 3D scene reconstruction can be affected by numerous uncertainty sources in real-world scenes. While Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (GS) achieve high-fidelity rendering, they lack built-in mechanisms to directl
Externí odkaz:
http://arxiv.org/abs/2409.06407
The underwater 3D scene reconstruction is a challenging, yet interesting problem with applications ranging from naval robots to VR experiences. The problem was successfully tackled by fully volumetric NeRF-based methods which can model both the geome
Externí odkaz:
http://arxiv.org/abs/2408.08206
This paper proposes a novel framework for large-scale scene reconstruction based on 3D Gaussian splatting (3DGS) and aims to address the scalability and accuracy challenges faced by existing methods. For tackling the scalability issue, we split the l
Externí odkaz:
http://arxiv.org/abs/2409.12774
The recent advent of 3D Gaussian Splatting (3DGS) has revolutionized the 3D scene reconstruction space enabling high-fidelity novel view synthesis in real-time. However, with the exception of RawNeRF, all prior 3DGS and NeRF-based methods rely on 8-b
Externí odkaz:
http://arxiv.org/abs/2407.16503
Vision-centric occupancy networks, which represent the surrounding environment with uniform voxels with semantics, have become a new trend for safe driving of camera-only autonomous driving perception systems, as they are able to detect obstacles reg
Externí odkaz:
http://arxiv.org/abs/2406.07037
Autor:
Szymanowicz, Stanislaw, Insafutdinov, Eldar, Zheng, Chuanxia, Campbell, Dylan, Henriques, João F., Rupprecht, Christian, Vedaldi, Andrea
In this paper, we propose Flash3D, a method for scene reconstruction and novel view synthesis from a single image which is both very generalisable and efficient. For generalisability, we start from a "foundation" model for monocular depth estimation
Externí odkaz:
http://arxiv.org/abs/2406.04343
3D reconstruction has been widely used in autonomous navigation fields of mobile robotics. However, the former research can only provide the basic geometry structure without the capability of open-world scene understanding, limiting advanced tasks li
Externí odkaz:
http://arxiv.org/abs/2403.11796
In this paper, we present a method to reconstruct the world and multiple dynamic humans in 3D from a monocular video input. As a key idea, we represent both the world and multiple humans via the recently emerging 3D Gaussian Splatting (3D-GS) represe
Externí odkaz:
http://arxiv.org/abs/2404.14410
Autor:
Dronova, Maria, Cheremnykh, Vladislav, Kotcov, Alexey, Fedoseev, Aleksey, Tsetserukou, Dzmitry
Current methods for 3D reconstruction and environmental mapping frequently face challenges in achieving high precision, highlighting the need for practical and effective solutions. In response to this issue, our study introduces FlyNeRF, a system int
Externí odkaz:
http://arxiv.org/abs/2404.12970