


High-gloss reflective terminator? Google NeRF-Casting: Ray tracing can do it!
NeRF is no longer "afraid" of near specular reflections
Early NeRF variants used multilayer perceptrons (MLPs) to map from 3D coordinates to volume density and viewpoints Related colors, but the large MLPs required to represent detailed 3D geometry and color are extremely slow to train and evaluate. Recent work has focused on making NeRF more efficient by replacing large MLPs with voxel grid-like data structures or a combination of grids and small MLPs. While it can be extended to represent detailed large-scale scenes, its advantages are limited to three-dimensional geometry and predominantly diffuse color. Expanding NeRF’s ability to model realistic viewpoint-dependent appearance remains a challenge. The current advanced model for view synthesis of shiny objects has limitations in two aspects:
can only synthesize accurate reflections of distant ambient lighting, while rendering close Poor performance in realistic reflections of scene content.
Relying on large MLPs to represent the viewpoint-related outgoing radiation of any point is difficult to scale to larger realistic scenes with detailed reflections.- NeRF-Casting is a method to solve these problems by introducing ray tracing into the NeRF rendering model. It mainly involves 3 areas:
Reflection modeling
: Traditional reflection modeling methods use physical laws and image-based techniques to represent surface reflection properties. In recent years, neural networks have been used to learn reflection properties, especially under complex materials and lighting conditions.- Ray Tracing: Ray tracing is a widely used computer graphics technique that produces realistic images by simulating the interaction of light with object surfaces. Ray tracing techniques have been used to generate high-quality reflection and refraction effects, but with high computational complexity.
- Three-dimensional imaging: Three-dimensional imaging technology involves generating three-dimensional representations from multi-view data. NeRF and other neural network methods have made significant progress in the field of 3D imaging by learning the 3D geometry and color distribution of a scene to generate new views.
Interested friends can watch the video effect: https://nerf-casting.github.io
NeRF-Casting’s Three main goals:
Want to model accurate, detailed reflections without relying on computationally expensive MLP evaluation.
Want to cast only a small amount of reflected light.- Hopefully minimize the amount of computation required to look up our representation at each point of these reflected rays.
- Based on Zip-NeRF[2]: Utilize a multi-scale hash grid to store 3D features, a small MLP (1 layer, width 64) to decode these features into densities, a larger MLP (3 layers, width 256) decodes these features into colors. This means that it is relatively cheap to query the density and characteristics of a sample along a ray. Given these constraints, the following process is followed to render a specular appearance:
- Use a small MLP to combine the accumulated reflection features with other sampled quantities (such as diffuse color features and blending weights for each sample) to generate a color value for each sample along the ray.
- Alpha composite these samples and densities to get the final color.
Reflection cone tracking
##Conical reflection feature
Now that a vMF distribution has been defined covering the reflection rays, the goal is to estimate the expected volume rendering characteristics on the vMF distribution and then decode it into reflection colors. This expected characteristic can be written as:Directional Sampling
The direction sampling described above helps select a small representative set of rays to average. However, for surfaces with high roughness, the sampled rays may be far apart relative to the underlying 3D grid cells. This means that the features in Equation 9 may be subject to artifacts, and small changes in the direction of the reflected ray may cause large changes in appearance.
In order to prevent this from happening, adjust the "feature weight reduction" technology in Zip-NeRF to the direction setting. This is achieved by multiplying features corresponding to voxels that are smaller compared to the vMF cone by a small multiplier, reducing their impact on the rendered color. According to Zip-NeRF, the weight-reducing feature at point x is defined as:
##Color decoder
The role of the color decoder is to assign a color to each sampling point along the ray, using the convexity of the two color components Combination:
The first color component Cv is similar to the typical NeRF perspective-dependent appearance model:
The second A component Cr, intended to simulate a glossy appearance, is calculated as follows:
Geometric representation and regularization
Appreciation of the effect
##To summarize
NeRF-Casting is a method of rendering scenes containing specular objects using Neural Radiation Fields (NeRF).Method: Reflection cones are reflected from surfaces in the scene and tracked via NeRF, combined with a novel set of techniques to anti-alias these reflections, enabling synthesis of distant and near Accurately detailed reflections of field content that move consistently and smoothly across surfaces.
Discussion: Quantitatively outperforms existing view synthesis techniques, especially for smooth surfaces showing detailed specular reflections. Qualitative visual improvements are far outweighed by quantitative improvements in image metrics. Of particular note is the smooth and consistent motion of reflections synthesized by this method, which is more realistic than the view-dependent appearance presented by baseline methods. This shows that standard image error metrics (PSNR, SSIM, etc.) are insufficient to assess the quality of view-dependent appearance.
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