We consider the scattering of light in participating media composed of sparsely and randomly distributed discrete particles. The particle size is expected to range from the scale of the wavelength to several orders of magnitude greater, resulting in an appearance with distinct graininess as opposed to the smooth appearance of continuous media. One fundamental issue in the physically-based synthesis of such appearance is to determine the necessary optical properties in every local region. Since these properties vary spatially, we resort to geometrical optics approximation (GOA), a highly efficient alternative to rigorous Lorenz—Mie theory, to quantitatively represent the scattering of a single particle. This enables us to quickly compute bulk optical properties for any particle size distribution. We then use a practical Monte Carlo rendering solution to solve energy transfer in the discrete participating media. Our proposed framework is the first to simulate a wide range of discrete participating media with different levels of graininess, converging to the continuous media case as the particle concentration increases.

Abstract Effective compression of densely sampled BRDF measurements is critical for many graphical or vision applications. In this paper, we present DeepBRDF, a deep‐learning‐based representation that can significantly reduce the dimensionality of measured BRDFs while enjoying high quality of recovery. We consider each measured BRDF as a sequence of image slices and design a deep autoencoder with a masked L 2 loss to discover a nonlinear low‐dimensional latent space of the high‐dimensional input data. Thorough experiments verify that the proposed method clearly outperforms PCA‐based strategies in BRDF data compression and is more robust. We demonstrate the effectiveness of DeepBRDF with two applications. For BRDF editing, we can easily create a new BRDF by navigating on the low‐dimensional manifold of DeepBRDF, guaranteeing smooth transitions and high physical plausibility. For BRDF recovery, we design another deep neural network to automatically generate the full BRDF data from a single input image. Aided by our DeepBRDF learned from real‐world materials, a wide range of reflectance behaviors can be recovered with high accuracy.

In this paper, we present an efficient framework for editing the appearance of measured materials. First, we separate the material data into diffuse and specular parts, which is important for measured BRDFs since editing without distinguishing the two parts makes the results less predictive. Additionally, the albedo of each part is calculated for later color editing. Next, we use a novel method to cluster the materials by their gloss levels and select the representative material of every level. With this at hand, the roughness, i.e., shape of the specular highlights can be adjusted among different levels. Finally, we reconstruct the edited materials which can be directly used in both online and offline rendering. We test the framework on the MERL dataset to validate the effectiveness of our method.

Transmission of radiation through spatially-correlated media has demonstrated deviations from the classical exponential law of the corresponding uncorrelated media. In this paper, we propose a general, physically-based method for modeling such correlated media with non-exponential decay of transmittance. We describe spatial correlations by introducing the Fractional Gaussian Field (FGF), a powerful mathematical tool that has proven useful in many areas but remains under-explored in graphics. With the FGF, we study the effects of correlations in a unified manner, by modeling both high-frequency, noise-like fluctuations and k -th order fractional Brownian motion (fBm) with a stochastic continuity property. As a result, we are able to reproduce a wide variety of appearances stemming from different types of spatial correlations. Compared to previous work, our method is the first that addresses both short-range and long-range correlations using physically-based fluctuation models. We show that our method can simulate different extents of randomness in spatially-correlated media, resulting in a smooth transition in a range of appearances from exponential falloff to complete transparency. We further demonstrate how our method can be integrated into an energy-conserving RTE framework with a well-designed importance sampling scheme and validate its ability compared to the classical transport theory and previous work.

Monte Carlo (MC) methods for light transport simulation are flexible and general but typically suffer from high variance and slow convergence. Gradientdomain rendering alleviates this problem by additionally generating image gradients and reformulating rendering as a screened Poisson image reconstruction problem. To improve the quality and performance of the reconstruction, we propose a novel and practical deep learning based approach in this paper. The core of our approach is a multi-branch auto-encoder, termed GradNet, which end-to-end learns a mapping from a noisy input image and its corresponding image gradients to a high-quality image with low variance. Once trained, our network is fast to evaluate and does not require manual parameter tweaking. Due to the difficulty in preparing ground-truth images for training, we design and train our network in a completely unsupervised manner by learning directly from the input data. This is the first solution incorporating unsupervised deep learning into the gradient-domain rendering framework. The loss function is defined as an energy function including a data fidelity term and a gradient fidelity term. To further reduce the noise of the reconstructed image, the loss function is reinforced by adding a regularizer constructed from selected rendering-specific features. We demonstrate that our method improves the reconstruction quality for a diverse set of scenes, and reconstructing a high-resolution image takes far less than one second on a recent GPU.

This paper introduces an architecture for segmenting 3D shapes into labeled semantic parts. Our architecture combines viewpoint selection method based on viewpoint entropy, multi-view image-based Fully Convolutional Networks (FCNs) and graph cuts optimization method to yield coherent segmentation of 3D shapes. First, we select iteratively a fixed number of perspectives with the maximum viewpoint entropy from existing viewpoints that can cover the shape's triangles, to maximally and automatically adjust the distance between the viewpoint and the center point of the shape to make sure the shape projected to fill the render window as wide as possible. Second, the image-based FCN is used for efficient view-based reasoning about 3D shape parts. In this process, global features generated by max view pooling are concatenated with every single view's feature in the fully connected layer before upsampling. Then, the multi-view FCN outputs confidence maps per part, which are then input into the projection layer that contains the mapping relationship of every shape's triangles and their projective pixels' positions in the rendered images from selected perspectives. And then, the FCN outputs are projected back onto 3D shape surfaces. and max view pooling is applied to the output of the projection layer so that every triangle of each shape has a unique probability for each label. Finally, graph cuts algorithm is implemented for the final segmentation result.