Mathias Paulin | Searcharxiv

arXiv2022

Harmonics Virtual Lights : fast projection of luminance field on spherical harmonics for efficient rendering

In this paper, we introduce Harmonics Virtual Lights (HVL), to model indirect light sources for interactive global illumination of dynamic 3D scenes. Virtual Point Lights (VPL) are an efficient approach to define indirect light sources and to evaluate the resulting indirect lighting. Nonetheless, VPL suffer from disturbing artifacts, especially with high frequency materials. Virtual Spherical Lights (VSL) avoid these artifacts by considering spheres instead of points but estimates the lighting integral using Monte Carlo which results to noise in the final image. We define HVL as an extension of VSL in a Spherical Harmonics (SH) framework, defining a closed form of the lighting integral evaluation. We propose an efficient SH projection of spherical lights contribution faster than existing methods. Computing the outgoing luminance requires $\mathcal{O}(n)$ operations when using materials with circular symmetric lobes, and $\mathcal{O}(n^2)$ operations for the general case, where $n$ is the number of SH bands. HVL can be used with either parametric or measured BRDF without extra cost and offers control over rendering time and image quality, by either decreasing or increasing the band limit used for SH projection. Our approach is particularly well designed to render medium-frequency one-bounce global illumination with arbitrary BRDF in interactive time.

cs.GR↗

arXiv2021

PCEDNet : A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds

In recent years, Convolutional Neural Networks (CNN) have proven to be efficient analysis tools for processing point clouds, e.g., for reconstruction, segmentation and classification. In this paper, we focus on the classification of edges in point clouds, where both edges and their surrounding are described. We propose a new parameterization adding to each point a set of differential information on its surrounding shape reconstructed at different scales. These parameters, stored in a Scale-Space Matrix (SSM), provide a well suited information from which an adequate neural network can learn the description of edges and use it to efficiently detect them in acquired point clouds. After successfully applying a multi-scale CNN on SSMs for the efficient classification of edges and their neighborhood, we propose a new lightweight neural network architecture outperforming the CNN in learning time, processing time and classification capabilities. Our architecture is compact, requires small learning sets, is very fast to train and classifies millions of points in seconds.

cs.GR↗