40-Issue 2

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https://diglib.eg.org/handle/10.2312/2633016

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Adversarial Single-Image SVBRDF Estimation with Hybrid Training
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Zhou, Xilong; Kalantari, Nima Khademi; Mitra, Niloy and Viola, Ivan
In this paper, we propose a deep learning approach for estimating the spatially-varying BRDFs (SVBRDF) from a single image. Most existing deep learning techniques use pixel-wise loss functions which limits the flexibility of the networks in handling this highly unconstrained problem. Moreover, since obtaining ground truth SVBRDF parameters is difficult, most methods typically train their networks on synthetic images and, therefore, do not effectively generalize to real examples. To avoid these limitations, we propose an adversarial framework to handle this application. Specifically, we estimate the material properties using an encoder-decoder convolutional neural network (CNN) and train it through a series of discriminators that distinguish the output of the network from ground truth. To address the gap in data distribution of synthetic and real images, we train our network on both synthetic and real examples. Specifically, we propose a strategy to train our network on pairs of real images of the same object with different lighting. We demonstrate that our approach is able to handle a variety of cases better than the state-of-the-art methods.
Automatic Surface Segmentation for Seamless Fabrication Using 4-axis Milling Machines
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Nuvoli, Stefano; Tola, Alessandro; Muntoni, Alessandro; Pietroni, Nico; Gobbetti, Enrico; Scateni, Riccardo; Mitra, Niloy and Viola, Ivan
We introduce a novel geometry-processing pipeline to guide the fabrication of complex shapes from a single block of material using 4-axis CNC milling machines. This setup extends classical 3-axis CNC machining with an extra degree of freedom to rotate the object around a fixed axis. The first step of our pipeline identifies the rotation axis that maximizes the overall fabrication accuracy. Then we identify two height-field regions at the rotation axis's extremes used to secure the block on the rotation tool. We segment the remaining portion of the mesh into a set of height-fields whose principal directions are orthogonal to the rotation axis. The segmentation balances the approximation quality, the boundary smoothness, and the total number of patches. Additionally, the segmentation process takes into account the object's geometric features, as well as saliency information. The output is a set of meshes ready to be processed by off-the-shelf software for the 3-axis tool-path generation. We present several results to demonstrate the quality and efficiency of our approach to a range of inputs
Blue Noise Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Onzenoodt, Christian van; Singh, Gurprit; Ropinski, Timo; Ritschel, Tobias; Mitra, Niloy and Viola, Ivan
We propose Blue Noise Plots, two-dimensional dot plots that depict data points of univariate data sets. While often onedimensional strip plots are used to depict such data, one of their main problems is visual clutter which results from overlap. To reduce this overlap, jitter plots were introduced, whereby an additional, non-encoding plot dimension is introduced, along which the data point representing dots are randomly perturbed. Unfortunately, this randomness can suggest non-existent clusters, and often leads to visually unappealing plots, in which overlap might still occur. To overcome these shortcomings, we introduce Blue Noise Plots where random jitter along the non-encoding plot dimension is replaced by optimizing all dots to keep a minimum distance in 2D i. e., Blue Noise. We evaluate the effectiveness as well as the aesthetics of Blue Noise Plots through both, a quantitative and a qualitative user study. The Python implementation of Blue Noise Plots is available here.
Coherent Mark-based Stylization of 3D Scenes at the Compositing Stage
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Garcia, Maxime; Vergne, Romain; Farhat, Mohamed-Amine; Bénard, Pierre; Noûs, Camille; Thollot, Joëlle; Mitra, Niloy and Viola, Ivan
We present a novel temporally coherent stylized rendering technique working entirely at the compositing stage. We first generate a distribution of 3D anchor points using an implicit grid based on the local object positions stored in a G-buffer, hence following object motion. We then draw splats in screen space anchored to these points so as to be motion coherent. To increase the perceived flatness of the style, we adjust the anchor points density using a fractalization mechanism. Sudden changes are prevented by controlling the anchor points opacity and introducing a new order-independent blending function. We demonstrate the versatility of our method by showing a large variety of styles thanks to the freedom offered by the splats content and their attributes that can be controlled by any G-buffer.
Correlation-Aware Multiple Importance Sampling for Bidirectional Rendering Algorithms
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Grittmann, Pascal; Georgiev, Iliyan; Slusallek, Philipp; Mitra, Niloy and Viola, Ivan
Combining diverse sampling techniques via multiple importance sampling (MIS) is key to achieving robustness in modern Monte Carlo light transport simulation. Many such methods additionally employ correlated path sampling to boost efficiency. Photon mapping, bidirectional path tracing, and path-reuse algorithms construct sets of paths that share a common prefix. This correlation is ignored by classical MIS heuristics, which can result in poor technique combination and noisy images.We propose a practical and robust solution to that problem. Our idea is to incorporate correlation knowledge into the balance heuristic, based on known path densities that are already required for MIS. This correlation-aware heuristic can achieve considerably lower error than the balance heuristic, while avoiding computational and memory overhead.
Curve Complexity Heuristic KD-trees for Neighborhood-based Exploration of 3D Curves
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Lu, Yucheng; Cheng, Luyu; Isenberg, Tobias; Fu, Chi-Wing; Chen, Guoning; Liu, Hui; Deussen, Oliver; Wang, Yunhai; Mitra, Niloy and Viola, Ivan
We introduce the curve complexity heuristic (CCH), a KD-tree construction strategy for 3D curves, which enables interactive exploration of neighborhoods in dense and large line datasets. It can be applied to searches of k-nearest curves (KNC) as well as radius-nearest curves (RNC). The CCH KD-tree construction consists of two steps: (i) 3D curve decomposition that takes into account curve complexity and (ii) KD-tree construction, which involves a novel splitting and early termination strategy. The obtained KD-tree allows us to improve the speed of existing neighborhood search approaches by at least an order of magnitude (i. e., 28× for KNC and 12× for RNC with 98% accuracy) by considering local curve complexity. We validate this performance with a quantitative evaluation of the quality of search results and computation time. Also, we demonstrate the usefulness of our approach for supporting various applications such as interactive line queries, line opacity optimization, and line abstraction.
Cyclostationary Gaussian Noise: Theory and Synthesis
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Lutz, Nicolas; Sauvage, Basile; Dischler, Jean-Michel; Mitra, Niloy and Viola, Ivan
Stationary Gaussian processes have been used for decades in the context of procedural noises to model and synthesize textures with no spatial organization. In this paper we investigate cyclostationary Gaussian processes, whose statistics are repeated periodically. It enables the modeling of noises having periodic spatial variations, which we call "cyclostationary Gaussian noises". We adapt to the cyclostationary context several stationary noises along with their synthesis algorithms: spot noise, Gabor noise, local random-phase noise, high-performance noise, and phasor noise. We exhibit real-time synthesis of a variety of visual patterns having periodic spatial variations.
Deep Detail Enhancement for Any Garment
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Zhang, Meng; Wang, Tuanfeng; Ceylan, Duygu; Mitra, Niloy J.; Mitra, Niloy and Viola, Ivan
Creating fine garment details requires significant efforts and huge computational resources. In contrast, a coarse shape may be easy to acquire in many scenarios (e.g., via low-resolution physically-based simulation, linear blend skinning driven by skeletal motion, portable scanners). In this paper, we show how to enhance, in a data-driven manner, rich yet plausible details starting from a coarse garment geometry. Once the parameterization of the garment is given, we formulate the task as a style transfer problem over the space of associated normal maps. In order to facilitate generalization across garment types and character motions, we introduce a patch-based formulation, that produces high-resolution details by matching a Gram matrix based style loss, to hallucinate geometric details (i.e., wrinkle density and shape). We extensively evaluate our method on a variety of production scenarios and show that our method is simple, light-weight, efficient, and generalizes across underlying garment types, sewing patterns, and body motion. Project page: http://geometry.cs.ucl.ac.uk/projects/2021/DeepDetailEnhance/
Deep HDR Estimation with Generative Detail Reconstruction
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Zhang, Yang; Aydin, Tunc O.; Mitra, Niloy and Viola, Ivan
We study the problem of High Dynamic Range (HDR) image reconstruction from a Standard Dynamic Range (SDR) input with potential clipping artifacts. Instead of building a direct model that maps from SDR to HDR images as in previous work, we decompose an input SDR image into a base (low frequency) and detail layer (high frequency), and treat reconstructing these two layers as two separate problems. We propose a novel architecture that comprises individual components specially designed to handle both tasks. Specifically, our base layer reconstruction component recovers low frequency content and remaps the color gamut of the input SDR, whereas our detail layer reconstruction component, which builds upon prior work on image inpainting, hallucinates missing texture information. The output HDR prediction is produced by a final refinement stage. We present qualitative and quantitative comparisons with existing techniques where our method achieves state-of-the-art performance.
Enabling Viewpoint Learning through Dynamic Label Generation
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Schelling, Michael; Hermosilla, Pedro; Vázquez, Pere-Pau; Ropinski, Timo; Mitra, Niloy and Viola, Ivan
Optimal viewpoint prediction is an essential task in many computer graphics applications. Unfortunately, common viewpoint qualities suffer from two major drawbacks: dependency on clean surface meshes, which are not always available, and the lack of closed-form expressions, which requires a costly search involving rendering. To overcome these limitations we propose to separate viewpoint selection from rendering through an end-to-end learning approach, whereby we reduce the influence of the mesh quality by predicting viewpoints from unstructured point clouds instead of polygonal meshes. While this makes our approach insensitive to the mesh discretization during evaluation, it only becomes possible when resolving label ambiguities that arise in this context. Therefore, we additionally propose to incorporate the label generation into the training procedure, making the label decision adaptive to the current network predictions. We show how our proposed approach allows for learning viewpoint predictions for models from different object categories and for different viewpoint qualities. Additionally, we show that prediction times are reduced from several minutes to a fraction of a second, as compared to state-of-the-art (SOTA) viewpoint quality evaluation. Code and training data is available at https://github.com/schellmi42/viewpoint_learning, which is to our knowledge the biggest viewpoint quality dataset available.
EUROGRAPHICS 2021: CGF 40-2 Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Mitra, Niloy; Viola, Ivan; Mitra, Niloy and Viola, Ivan
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Fast Updates for Least-Squares Rotational Alignment
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Zhang, Jiayi Eris; Jacobson, Alec; Alexa, Marc; Mitra, Niloy and Viola, Ivan
Across computer graphics, vision, robotics and simulation, many applications rely on determining the 3D rotation that aligns two objects or sets of points. The standard solution is to use singular value decomposition (SVD), where the optimal rotation is recovered as the product of the singular vectors. Faster computation of only the rotation is possible using suitable parameterizations of the rotations and iterative optimization. We propose such a method based on the Cayley transformations. The resulting optimization problem allows better local quadratic approximation compared to the Taylor approximation of the exponential map. This results in both faster convergence as well as more stable approximation compared to other iterative approaches. It also maps well to AVX vectorization. We compare our implementation with a wide range of alternatives on real and synthetic data. The results demonstrate up to two orders of magnitude of speedup compared to a straightforward SVD implementation and a 1.5-6 times speedup over popular optimized code.
Geometric Construction of Auxetic Metamaterials
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Bonneau, Georges-Pierre; Hahmann, Stefanie; Marku, Johana; Mitra, Niloy and Viola, Ivan
This paper is devoted to a category of metamaterials called auxetics, identified by their negative Poisson's ratio. Our work consists in exploring geometrical strategies to generate irregular auxetic structures. More precisely we seek to reduce the Poisson's ratio n, by pruning an irregular network based solely on geometric criteria. We introduce a strategy combining a pure geometric pruning algorithm followed by a physics-based testing phase to determine the resulting Poisson's ratio of our structures. We propose an algorithm that generates sets of irregular auxetic networks. Our contributions include geometrical characterization of auxetic networks, development of a pruning strategy, generation of auxetic networks with low Poisson's ratio, as well as validation of our approach.We provide statistical validation of our approach on large sets of irregular networks, and we additionally laser-cut auxetic networks in sheets of rubber. The findings reported here show that it is possible to reduce the Poisson's ratio by geometric pruning, and that we can generate irregular auxetic networks at lower processing times than a physics-based approach.
Hierarchical Raster Occlusion Culling
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Lee, Gi Beom; Jeong, Moonsoo; Seok, Yechan; Lee, Sungkil; Mitra, Niloy and Viola, Ivan
This paper presents a scalable online occlusion culling algorithm, which significantly improves the previous raster occlusion culling using object-level bounding volume hierarchy. Given occluders found with temporal coherence, we find and rasterize coarse groups of potential occludees in the hierarchy. Within the rasterized bounds, per-pixel ray casting tests fine-grained visibilities of every individual occludees. We further propose acceleration techniques including the read-back of counters for tightly-packed multidrawing and occluder filtering. Our solution requires only constant draw calls for batch occlusion tests, while avoiding costly iteration for hierarchy traversal. Our experiments prove our solution outperforms the existing solutions in terms of scalability, culling efficiency, and occlusion-query performance.
Higher Dimensional Graphics: Conceiving Worlds in Four Spatial Dimensions and Beyond
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Cavallo, Marco; Mitra, Niloy and Viola, Ivan
While the interpretation of high-dimensional datasets has become a necessity in most industries, the spatial visualization of higher-dimensional geometry has mostly remained a niche research topic for mathematicians and physicists. Intermittent contributions to this field date back more than a century, and have had a non-negligible influence on contemporary art and philosophy. However, most contributions have focused on the understanding of specific mathematical shapes, with few concrete applications. In this work, we attempt to revive the community's interest in visualizing higher dimensional geometry by shifting the focus from the visualization of abstract shapes to the design of a broader hyper-universe concept, wherein 3D and 4D objects can coexist and interact with each other. Specifically, we discuss the content definition, authoring patterns, and technical implementations associated with the process of extending standard 3D applications as to support 4D mechanics. We operationalize our ideas through the introduction of a new hybrid 3D/4D videogame called Across Dimensions, which we developed in Unity3D through the integration of our own 4D plugin.
Honey, I Shrunk the Domain: Frequency-aware Force Field Reduction for Efficient Fluids Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Tang, Jingwei; Azevedo, Vinicius C.; Cordonnier, Guillaume; Solenthaler, Barbara; Mitra, Niloy and Viola, Ivan
Fluid control often uses optimization of control forces that are added to a simulation at each time step, such that the final animation matches a single or multiple target density keyframes provided by an artist. The optimization problem is strongly under-constrained with a high-dimensional parameter space, and finding optimal solutions is challenging, especially for higher resolution simulations. In this paper, we propose two novel ideas that jointly tackle the lack of constraints and high dimensionality of the parameter space. We first consider the fact that optimized forces are allowed to have divergent modes during the optimization process. These divergent modes are not entirely projected out by the pressure solver step, manifesting as unphysical smoke sources that are explored by the optimizer to match a desired target. Thus, we reduce the space of the possible forces to the family of strictly divergence-free velocity fields, by optimizing directly for a vector potential. We synergistically combine this with a smoothness regularization based on a spectral decomposition of control force fields. Our method enforces lower frequencies of the force fields to be optimized first by filtering force frequencies in the Fourier domain. The mask-growing strategy is inspired by Kolmogorov's theory about scales of turbulence. We demonstrate improved results for 2D and 3D fluid control especially in higher-resolution settings, while eliminating the need for manual parameter tuning. We showcase various applications of our method, where the user effectively creates or edits smoke simulations.
Interactive Photo Editing on Smartphones via Intrinsic Decomposition
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Shekhar, Sumit; Reimann, Max; Mayer, Maximilian; Semmo, Amir; Pasewaldt, Sebastian; Döllner, Jürgen; Trapp, Matthias; Mitra, Niloy and Viola, Ivan
Intrinsic decomposition refers to the problem of estimating scene characteristics, such as albedo and shading, when one view or multiple views of a scene are provided. The inverse problem setting, where multiple unknowns are solved given a single known pixel-value, is highly under-constrained. When provided with correlating image and depth data, intrinsic scene decomposition can be facilitated using depth-based priors, which nowadays is easy to acquire with high-end smartphones by utilizing their depth sensors. In this work, we present a system for intrinsic decomposition of RGB-D images on smartphones and the algorithmic as well as design choices therein. Unlike state-of-the-art methods that assume only diffuse reflectance, we consider both diffuse and specular pixels. For this purpose, we present a novel specularity extraction algorithm based on a multi-scale intensity decomposition and chroma inpainting. At this, the diffuse component is further decomposed into albedo and shading components. We use an inertial proximal algorithm for non-convex optimization (iPiano) to ensure albedo sparsity. Our GPUbased visual processing is implemented on iOS via the Metal API and enables interactive performance on an iPhone 11 Pro. Further, a qualitative evaluation shows that we are able to obtain high-quality outputs. Furthermore, our proposed approach for specularity removal outperforms state-of-the-art approaches for real-world images, while our albedo and shading layer decomposition is faster than the prior work at a comparable output quality. Manifold applications such as recoloring, retexturing, relighting, appearance editing, and stylization are shown, each using the intrinsic layers obtained with our method and/or the corresponding depth data.
Layout Embedding via Combinatorial Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Born, Janis; Schmidt, Patrick; Kobbelt, Leif; Mitra, Niloy and Viola, Ivan
We consider the problem of injectively embedding a given graph connectivity (a layout) into a target surface. Starting from prescribed positions of layout vertices, the task is to embed all layout edges as intersection-free paths on the surface. Besides merely geometric choices (the shape of paths) this problem is especially challenging due to its topological degrees of freedom (how to route paths around layout vertices). The problem is typically addressed through a sequence of shortest path insertions, ordered by a greedy heuristic. Such insertion sequences are not guaranteed to be optimal: Early path insertions can potentially force later paths into unexpected homotopy classes. We show how common greedy methods can easily produce embeddings of dramatically bad quality, rendering such methods unsuitable for automatic processing pipelines. Instead, we strive to find the optimal order of insertions, i.e. the one that minimizes the total path length of the embedding. We demonstrate that, despite the vast combinatorial solution space, this problem can be effectively solved on simply-connected domains via a custom-tailored branch-and-bound strategy. This enables directly using the resulting embeddings in downstream applications which cannot recover from initializations in a wrong homotopy class. We demonstrate the robustness of our method on a shape dataset by embedding a common template layout per category, and show applications in quad meshing and inter-surface mapping.
Learning and Exploring Motor Skills with Spacetime Bounds
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Ma, Li-Ke; Yang, Zeshi; Tong, Xin; Guo, Baining; Yin, KangKang; Mitra, Niloy and Viola, Ivan
Equipping characters with diverse motor skills is the current bottleneck of physics-based character animation. We propose a Deep Reinforcement Learning (DRL) framework that enables physics-based characters to learn and explore motor skills from reference motions. The key insight is to use loose space-time constraints, termed spacetime bounds, to limit the search space in an early termination fashion. As we only rely on the reference to specify loose spacetime bounds, our learning is more robust with respect to low quality references. Moreover, spacetime bounds are hard constraints that improve learning of challenging motion segments, which can be ignored by imitation-only learning. We compare our method with state-of-the-art tracking-based DRL methods. We also show how to guide style exploration within the proposed framework.
Learning Human Search Behavior from Egocentric Visual Inputs
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Sorokin, Maks; Yu, Wenhao; Ha, Sehoon; Liu, C. Karen; Mitra, Niloy and Viola, Ivan
''Looking for things'' is a mundane but critical task we repeatedly carry on in our daily life. We introduce a method to develop a human character capable of searching for a randomly located target object in a detailed 3D scene using its locomotion capability and egocentric vision perception represented as RGBD images. By depriving the privileged 3D information from the human character, it is forced to move and look around simultaneously to account for the restricted sensing capability, resulting in natural navigation and search behaviors. Our method consists of two components: 1) a search control policy based on an abstract character model, and 2) an online replanning control module for synthesizing detailed kinematic motion based on the trajectories planned by the search policy. We demonstrate that the combined techniques enable the character to effectively find often occluded household items in indoor environments. The same search policy can be applied to different full body characters without the need of retraining. We evaluate our method quantitatively by testing it on randomly generated scenarios. Our work is a first step toward creating intelligent virtual agents with humanlike behaviors driven by onboard sensors, paving the road toward future robotic applications.

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