3D Representations

Neural Radiance Field

Introduced by Mildenhall et al. in NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

NeRF represents a scene with learned, continuous volumetric radiance field $F_\theta$ defined over a bounded 3D volume. In a NeRF, $F_\theta$ is a multilayer perceptron (MLP) that takes as input a 3D position $x = (x, y, z)$ and unit-norm viewing direction $d = (dx, dy, dz)$, and produces as output a density $\sigma$ and color $c = (r, g, b)$. The weights of the multilayer perceptron that parameterize $F_\theta$ are optimized so as to encode the radiance field of the scene. Volume rendering is used to compute the color of a single pixel.

Source: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Novel View Synthesis 137 20.95%
3D Reconstruction 53 8.10%
Text to 3D 27 4.13%
Neural Rendering 27 4.13%
3D Generation 26 3.98%
Pose Estimation 20 3.06%
Depth Estimation 19 2.91%
Image Generation 18 2.75%
Semantic Segmentation 18 2.75%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

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