Rendering - Experimental Ideas & Implementations 2018

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https://diglib.eg.org/handle/10.2312/2632569

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Approximate svBRDF Estimation From Mobile Phone Video
(The Eurographics Association, 2018) Albert, Rachel A.; Chan, Dorian Yao; Goldman, Dan B.; O'Brien, James F.; Jakob, Wenzel and Hachisuka, Toshiya
We describe a new technique for obtaining a spatially varying BRDF (svBRDF) of a flat object using printed fiducial markers and a cell phone capable of continuous flash video. Our homography-based video frame alignment method does not require the fiducial markers to be visible in every frame, thereby enabling us to capture larger areas at a closer distance and higher resolution than in previous work. Pixels in the resulting panorama are fit with a BRDF based on a recursive subdivision algorithm, utilizing all the light and view positions obtained from the video. We show the versatility of our method by capturing a variety of materials with both one and two camera input streams and rendering our results on 3D objects under complex illumination.
Deep Hybrid Real and Synthetic Training for Intrinsic Decomposition
(The Eurographics Association, 2018) Bi, Sai; Kalantari, Nima Khademi; Ramamoorthi, Ravi; Jakob, Wenzel and Hachisuka, Toshiya
Intrinsic image decomposition is the process of separating the reflectance and shading layers of an image, which is a challenging and underdetermined problem. In this paper, we propose to systematically address this problem using a deep convolutional neural network (CNN). Although deep learning (DL) has been recently used to handle this application, the current DL methods train the network only on synthetic images as obtaining ground truth reflectance and shading for real images is difficult. Therefore, these methods fail to produce reasonable results on real images and often perform worse than the non-DL techniques. We overcome this limitation by proposing a novel hybrid approach to train our network on both synthetic and real images. Specifically, in addition to directly supervising the network using synthetic images, we train the network by enforcing it to produce the same reflectance for a pair of images of the same real-world scene with different illuminations. Furthermore, we improve the results by incorporating a bilateral solver layer into our system during both training and test stages. Experimental results show that our approach produces better results than the state-of-the-art DL and non-DL methods on various synthetic and real datasets both visually and numerically.
Diffuse-Specular Separation using Binary Spherical Gradient Illumination
(The Eurographics Association, 2018) Kampouris, Christos; Zafeiriou, Stefanos; Ghosh, Abhijeet; Jakob, Wenzel and Hachisuka, Toshiya
We introduce a novel method for view-independent diffuse-specular separation of albedo and photometric normals without requiring polarization using binary spherical gradient illumination. The key idea is that with binary gradient illumination, a dielectric surface oriented towards the dark hemisphere exhibits pure diffuse reflectance while a surface oriented towards the bright hemisphere exhibits both diffuse and specular reflectance. We exploit this observation to formulate diffuse-specular separation based on color-space analysis of a surface's response to binary spherical gradients and their complements. The method does not impose restrictions on viewpoints and requires fewer photographs for multiview acquisition than polarized spherical gradient illumination. We further demonstrate an efficient two-shot capture using spectral multiplexing of the illumination that enables diffuse-specular separation of albedo and heuristic separation of photometric normals.
Eurographics Symposium on Rendering - Experimental Ideas and Implementations: Frontmatter
(The Eurographics Association, 2018) Jakob, Wenzel; Hachisuka, Toshiya; Jakob, Wenzel and Hachisuka, Toshiya
An Improved Multiple Importance Sampling Heuristic for Density Estimates in Light Transport Simulations
(The Eurographics Association, 2018) Jendersie, Johannes; Grosch, Thorsten; Jakob, Wenzel and Hachisuka, Toshiya
Vertex connection and merging (VCM) is one of the most robust light transport simulation algorithms developed so far. It combines bidirectional path tracing with photon mapping using multiple importance sampling (MIS). However, there are scene setups where the current weight computation is not optimal. If different merge events on a single path have roughly the same likelihood to be found, but different photon densities, this leads to high variance samples. We show how to improve the heuristic for density estimation events to overcome this issue by including the photon density into the MIS computation. This leads to a faster convergence in VCM and related techniques. The proposed change is easy to implement and is orthogonal to other improvements of the algorithm.
Matrix Bidirectional Path Tracing
(The Eurographics Association, 2018) Chaitanya, Chakravarty Reddy Alla; Belcour, Laurent; Hachisuka, Toshiya; Premoze, Simon; Pantaleoni, Jacopo; Nowrouzezahrai, Derek; Jakob, Wenzel and Hachisuka, Toshiya
Sampled paths in Monte Carlo ray tracing can be arbitrarily close to each other due to its stochastic nature. Such clumped samples in the path space tend to contribute little toward an accurate estimate of each pixel. Bidirectional light transport methods make this issue further complicated since connecting paths of sampled subpaths can be arbitrarily clumped again. We propose a matrix formulation of bidirectional light transport that enables stratification (and low-discrepancy sampling) in this connection space. This stratification allows us to distribute computation evenly across contributing paths in the image, which is not possible with standard bidirectional or Markov chain solutions. Each element in our matrix formulation represents a pair of connected eye- and light-subpaths. By carefully reordering these elements, we build a 2D space where equally contributing paths are distributed coherently. We devise an unbiased rendering algorithm that leverages this coherence to effectively sample path space, consistently achieving a 2-3x speedup in radiometrically complex scenes compared to the state-of-the-art.
PN-Method for Multiple Scattering in Participating Media
(The Eurographics Association, 2018) Koerner, David; Portsmouth, Jamie; Jakob, Wenzel; Jakob, Wenzel and Hachisuka, Toshiya
Rendering highly scattering participating media using brute force path tracing is a challenge. The diffusion approximation reduces the problem to solving a simple linear partial differential equation. Flux-limited diffusion introduces nonlinearities to alleviate the approximation error but introduces several ad-hoc assumptions. Both methods are based on the spherical harmonics expansion of the radiance field, that is truncated after the first order. In this paper, we investigate the open question of whether higher orders provide a viable alternative to these two approaches. Increasing the order introduces a set of increasingly complex coupled partial differential equations, whose growing number and complexity make them very difficult to work with. We use a computer algebra framework for representing and manipulating the underlying mathematical equations and use it to derive the time-independent real-valued PN-equations for arbitrary orders. We further present a staggered-grid PN-solver and generate its stencil code directly from the expression tree of the PN-equations. Finally, we discuss how our method compares against prior work for various standard problems. We will release our computer algebra system, solver, and data as open source to ensure reproducibility of all of our results.
Primary Sample Space Path Guiding
(The Eurographics Association, 2018) Guo, Jerry Jinfeng; Bauszat, Pablo; Bikker, Jacco; Eisemann, Elmar; Jakob, Wenzel and Hachisuka, Toshiya
Guiding path tracing in light transport simulation has been one of the practical choices for variance reduction in production rendering. For this purpose, typically structures in the spatial-directional domain are built. We present a novel scheme for unbiased path guiding. Different from existing methods, we work in primary sample space. We collect records of primary samples as well as the luminance that the resulting path contributes and build a multiple dimensional structure, from which we derive random numbers that are fed into the path tracer. This scheme is executed completely outside the rendering kernel. We demonstrate that this method is practical and efficient. We manage to reduce variance and zero radiance paths by only working in the primary sample space.
Scalable Real-Time Shadows using Clustering and Metric Trees
(The Eurographics Association, 2018) Deves, François; Mora, Frédéric; Aveneau, Lilian; Ghazanfarpour, Djamchid; Jakob, Wenzel and Hachisuka, Toshiya
Real-time shadow algorithms based on geometry generally produce high quality shadows. Recent works have considerably improved their efficiency. However, scalability remains an issue because these methods strongly depend on the geometric complexity. This paper focuses on this problem. We present a new real-time shadow algorithm for non-deformable models that scales the geometric complexity. Our method groups triangles into clusters by precomputing bounding spheres or bounding capsules (line-swept spheres). At each frame, we build a ternary metric tree to partition the spheres and capsules according to their apparent distance from the light. Then, this tree is used as an acceleration data structure to determine the visibility of the light for each image point. While clustering allows to scale down the geometric complexity, metric trees allow to encode the bounding volumes of the clusters in a hierarchical data structure. Our experiments show that our approach remains efficient, including with models with over 70 million triangles.
Screen Space Approximate Gaussian Hulls
(The Eurographics Association, 2018) Meder, Julian; Brüderlin, Beat; Jakob, Wenzel and Hachisuka, Toshiya
The Screen Space Approximate Gaussian Hull method presented in this paper is based on an output sensitive, adaptive approach, which addresses the challenge of high quality rendering even for high resolution displays and large numbers of light sources or indirect lighting. Our approach uses dynamically sparse sampling of the light information on a low-resolution mesh approximated from screen space and applying these samples in a deferred shading stage to the full resolution image. This preserves geometric detail unlike common approaches using lower resolution rendering combined with upsampling strategies. The light samples are expressed by spherical Gaussian distribution functions, for which we found a more precise closed form integration compared to existing approaches. Thus, our method does not exhibit the quality degradation shown by previously proposed approaches and we show that the implementation is very efficient. Moreover, being an output sensitive approach, it can be used for massive scene rendering without additional cost.
Soft Transparency for Point Cloud Rendering
(The Eurographics Association, 2018) Seemann, Patrick; Palma, Gianpaolo; Dellepiane, Matteo; Cignoni, Paolo; Goesele, Michael; Jakob, Wenzel and Hachisuka, Toshiya
We propose a novel rendering framework for visualizing point data with complex structures and/or different quality of data. The point cloud can be characterized by setting a per-point scalar field associated to the aspect that differentiates the parts of the dataset (i.e. uncertainty given by local normal variation). Our rendering method uses the scalar field to render points as solid splats or semi-transparent spheres with non-uniform density to produce the final image. To that end, we derive a base model for integrating density in (intersecting) spheres for both the uniform and non-uniform setting and introduce a simple and fast approximation which yields interactive rendering speeds for millions of points. Because our method only relies on the basic OpenGL rasterization pipeline, rendering properties can be adjusted in real-time by user. The method has been tested on several datasets with different characteristics, and user studies show that a clearer understanding of the scene is possible in comparison with point splatting techniques and basic transparency rendering.
A Unified Manifold Framework for Efficient BRDF Sampling based on Parametric Mixture Models
(The Eurographics Association, 2018) Herholz, Sebastian; Elek, Oskar; Schindel, Jens; Křivánek, Jaroslav; Lensch, Hendrik P. A.; Jakob, Wenzel and Hachisuka, Toshiya
Virtually all existing analytic BRDF models are built from multiple functional components (e.g., Fresnel term, normal distribution function, etc.). This makes accurate importance sampling of the full model challenging, and so current solutions only cover a subset of the model's components. This leads to sub-optimal or even invalid proposed directional samples, which can negatively impact the efficiency of light transport solvers based on Monte Carlo integration. To overcome this problem, we propose a unified BRDF sampling strategy based on parametric mixture models (PMMs). We show that for a given BRDF, the parameters of the associated PMM can be defined in smooth manifold spaces, which can be compactly represented using multivariate B-Splines. These manifolds are defined in the parameter space of the BRDF and allow for arbitrary, continuous queries of the PMM representation for varying BRDF parameters, which further enables importance sampling for spatially varying BRDFs. Our representation is not limited to analytic BRDF models, but can also be used for sampling measured BRDF data. The resulting manifold framework enables accurate and efficient BRDF importance sampling with very small approximation errors.

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