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 131 17.99%
3D Reconstruction 71 9.75%
Neural Rendering 27 3.71%
Autonomous Driving 24 3.30%
Pose Estimation 24 3.30%
3D Generation 21 2.88%
3D Scene Reconstruction 19 2.61%
Object 18 2.47%
Text to 3D 16 2.20%

Components


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

Categories