Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Tancik, Matthew"'
Autor:
Ye, Vickie, Li, Ruilong, Kerr, Justin, Turkulainen, Matias, Yi, Brent, Pan, Zhuoyang, Seiskari, Otto, Ye, Jianbo, Hu, Jeffrey, Tancik, Matthew, Kanazawa, Angjoo
gsplat is an open-source library designed for training and developing Gaussian Splatting methods. It features a front-end with Python bindings compatible with the PyTorch library and a back-end with highly optimized CUDA kernels. gsplat offers numero
Externí odkaz:
http://arxiv.org/abs/2409.06765
Publikováno v:
ICCV 2023
Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering. Recent works have included alternative sampling approaches to help accelerate their methods, however, they ar
Externí odkaz:
http://arxiv.org/abs/2305.04966
Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory. Existing evaluation protocols often do not capture these effects, since they usually only assess i
Externí odkaz:
http://arxiv.org/abs/2304.10532
We propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, our method uses an image-conditioned diffusion model (InstructPix2Pix) to iteratively edit the input imag
Externí odkaz:
http://arxiv.org/abs/2303.12789
Humans describe the physical world using natural language to refer to specific 3D locations based on a vast range of properties: visual appearance, semantics, abstract associations, or actionable affordances. In this work we propose Language Embedded
Externí odkaz:
http://arxiv.org/abs/2303.09553
Autor:
Tancik, Matthew, Weber, Ethan, Ng, Evonne, Li, Ruilong, Yi, Brent, Kerr, Justin, Wang, Terrance, Kristoffersen, Alexander, Austin, Jake, Salahi, Kamyar, Ahuja, Abhik, McAllister, David, Kanazawa, Angjoo
Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more. In order to streamline the development and deployment of NeRF research, we propose a modular PyTorch
Externí odkaz:
http://arxiv.org/abs/2302.04264
We propose NerfAcc, a toolbox for efficient volumetric rendering of radiance fields. We build on the techniques proposed in Instant-NGP, and extend these techniques to not only support bounded static scenes, but also for dynamic scenes and unbounded
Externí odkaz:
http://arxiv.org/abs/2210.04847
TV shows depict a wide variety of human behaviors and have been studied extensively for their potential to be a rich source of data for many applications. However, the majority of the existing work focuses on 2D recognition tasks. In this paper, we m
Externí odkaz:
http://arxiv.org/abs/2207.14279
Autor:
Tancik, Matthew, Casser, Vincent, Yan, Xinchen, Pradhan, Sabeek, Mildenhall, Ben, Srinivasan, Pratul P., Barron, Jonathan T., Kretzschmar, Henrik
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments. Specifically, we demonstrate that when scaling NeRF to render city-scale scenes spanning multiple blocks, it is vital to decompose the scene into
Externí odkaz:
http://arxiv.org/abs/2202.05263
Autor:
Yu, Alex, Fridovich-Keil, Sara, Tancik, Matthew, Chen, Qinhong, Recht, Benjamin, Kanazawa, Angjoo
We introduce Plenoxels (plenoptic voxels), a system for photorealistic view synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical harmonics. This representation can be optimized from calibrated images via gradient methods and regu
Externí odkaz:
http://arxiv.org/abs/2112.05131