Search Results for author: Daniel Rebain

Found 13 papers, 1 papers with code

BANF: Band-limited Neural Fields for Levels of Detail Reconstruction

no code implementations CVPR 2024 Ahan Shabanov, Shrisudhan Govindarajan, Cody Reading, Lily Goli, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi

Largely due to their implicit nature, neural fields lack a direct mechanism for filtering, as Fourier analysis from discrete signal processing is not directly applicable to these representations.

Evaluating Alternatives to SFM Point Cloud Initialization for Gaussian Splatting

no code implementations18 Apr 2024 Yalda Foroutan, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi

3D Gaussian Splatting has recently been embraced as a versatile and effective method for scene reconstruction and novel view synthesis, owing to its high-quality results and compatibility with hardware rasterization.

Novel View Synthesis

3D Gaussian Splatting as Markov Chain Monte Carlo

no code implementations15 Apr 2024 Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun, Jeff Tseng, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, Kwang Moo Yi

While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which does not always generalize and may lead to poor-quality renderings.

Neural Rendering

Neural Fields as Distributions: Signal Processing Beyond Euclidean Space

no code implementations CVPR 2024 Daniel Rebain, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi

We demonstrate how this framework can enable novel integrations of signal processing techniques for neural field applications on both Euclidean domains such as images and audio as well as non-Euclidean domains such as rotations and rays.

Volumetric Rendering with Baked Quadrature Fields

no code implementations2 Dec 2023 Gopal Sharma, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi

We propose a novel Neural Radiance Field (NeRF) representation for non-opaque scenes that allows fast inference by utilizing textured polygons.

nerf2nerf: Pairwise Registration of Neural Radiance Fields

no code implementations3 Nov 2022 Lily Goli, Daniel Rebain, Sara Sabour, Animesh Garg, Andrea Tagliasacchi

We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i. e. ICP) to operate on Neural Radiance Fields (NeRF) -- neural 3D scene representations trained from collections of calibrated images.

LOLNeRF: Learn from One Look

no code implementations CVPR 2022 Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, Andrea Tagliasacchi

We present a method for learning a generative 3D model based on neural radiance fields, trained solely from data with only single views of each object.

Depth Estimation Depth Prediction +1

Deep Medial Fields

no code implementations7 Jun 2021 Daniel Rebain, Ke Li, Vincent Sitzmann, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi

Implicit representations of geometry, such as occupancy fields or signed distance fields (SDF), have recently re-gained popularity in encoding 3D solid shape in a functional form.

DeRF: Decomposed Radiance Fields

no code implementations CVPR 2021 Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi

Moreover, we show that a Voronoi spatial decomposition is preferable for this purpose, as it is provably compatible with the Painter's Algorithm for efficient and GPU-friendly rendering.

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