![]() ![]() This is a volumetric representation for a 2D volume it does not deal with any lines or curves (which are surfaces in 2D). First, we can parametrize the color C of a point (x, y) as a mathematical function C = f(x, y). There are several ways to use mathematical functions. Let’s look at functional representations (sometimes also called implicit ). Is this all? No, there are more ways to represent an image mathematically. The third option is a point cloud, a cloud of geometric points, which can be represented as a list of coordinates (x, y) of each point or (x, y, c) if the points are colored. On the other hand, a vector image is composed of geometric shapes such as lines, circles and curves. Most often, we use a pixel image, e.g., a square grid of tiny colored squares, implemented in formats like PNG and JPEG. Representation of 2D images: a) – pixels, b) – vector, c) – point cloud, d) – functional How to represent a 2D image on a computer? There are several ways: NeRF review article (likewise autumn 2022)īut before we continue with NeRF, let’s start with a simpler problem: 2D images.ECCV 2022 NeRF tutorial (Autumn 2022, not too old at the moment of writing this).If you want to learn more about Neural Radiance Fields, we strongly recommend the following resources in this order: Ouch, this sounds scary? Don’t worry, We will explain the idea slowly as we go along. NeRF (proposed in the original 2020 paper ) is the technique to represent a 3D scene volumetrically (i.e., without any surfaces) as a function parametrized by a neural network to render 2D views of such a scene and to train the network on a set 2D views. We will conclude the article with the practical part: using NeRFStudio for training and rendering NeRF on a home computer or cloud. We start with basic NeRF theory, followed by NeRF limitations and the possible ways to overcome them. In this article, we will give a brief beginner-level introduction to neural radiance fields (NeRF).
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