@article{11735,
abstract = {Interlocking puzzles are intriguing geometric games where the puzzle pieces are held together based on their geometric arrangement, preventing the puzzle from falling apart. High-level-of-difficulty, or simply high-level, interlocking puzzles are a subclass of interlocking puzzles that require multiple moves to take out the first subassembly from the puzzle. Solving a high-level interlocking puzzle is a challenging task since one has to explore many different configurations of the puzzle pieces until reaching a configuration where the first subassembly can be taken out. Designing a high-level interlocking puzzle with a user-specified level of difficulty is even harder since the puzzle pieces have to be interlocking in all the configurations before the first subassembly is taken out.
In this paper, we present a computational approach to design high-level interlocking puzzles. The core idea is to represent all possible configurations of an interlocking puzzle as well as transitions among these configurations using a rooted, undirected graph called a disassembly graph and leverage this graph to find a disassembly plan that requires a minimal number of moves to take out the first subassembly from the puzzle. At the design stage, our algorithm iteratively constructs the geometry of each puzzle piece to expand the disassembly graph incrementally, aiming to achieve a user-specified level of difficulty. We show that our approach allows efficient generation of high-level interlocking puzzles of various shape complexities, including new solutions not attainable by state-of-the-art approaches.},
author = {Chen, Rulin and Wang, Ziqi and Song, Peng and Bickel, Bernd},
issn = {1557-7368},
journal = {ACM Transactions on Graphics},
number = {4},
publisher = {Association for Computing Machinery},
title = {{Computational design of high-level interlocking puzzles}},
doi = {10.1145/3528223.3530071},
volume = {41},
year = {2022},
}
@article{11993,
abstract = {Moulding refers to a set of manufacturing techniques in which a mould, usually a cavity or a solid frame, is used to shape a liquid or pliable material into an object of the desired shape. The popularity of moulding comes from its effectiveness, scalability and versatility in terms of employed materials. Its relevance as a fabrication process is demonstrated by the extensive literature covering different aspects related to mould design, from material flow simulation to the automation of mould geometry design. In this state-of-the-art report, we provide an extensive review of the automatic methods for the design of moulds, focusing on contributions from a geometric perspective. We classify existing mould design methods based on their computational approach and the nature of their target moulding process. We summarize the relationships between computational approaches and moulding techniques, highlighting their strengths and limitations. Finally, we discuss potential future research directions.},
author = {Alderighi, Thomas and Malomo, Luigi and Auzinger, Thomas and Bickel, Bernd and Cignoni, Paulo and Pietroni, Nico},
issn = {1467-8659},
journal = {Computer Graphics Forum},
keywords = {Computer Graphics and Computer-Aided Design},
number = {6},
pages = {435--452},
publisher = {Wiley},
title = {{State of the art in computational mould design}},
doi = {10.1111/cgf.14581},
volume = {41},
year = {2022},
}
@inproceedings{12135,
abstract = {A good match of material appearance between real-world objects and their digital on-screen representations is critical for many applications such as fabrication, design, and e-commerce. However, faithful appearance reproduction is challenging, especially for complex phenomena, such as gloss. In most cases, the view-dependent nature of gloss and the range of luminance values required for reproducing glossy materials exceeds the current capabilities of display devices. As a result, appearance reproduction poses significant problems even with accurately rendered images. This paper studies the gap between the gloss perceived from real-world objects and their digital counterparts. Based on our psychophysical experiments on a wide range of 3D printed samples and their corresponding photographs, we derive insights on the influence of geometry, illumination, and the display’s brightness and measure the change in gloss appearance due to the display limitations. Our evaluation experiments demonstrate that using the prediction to correct material parameters in a rendering system improves the match of gloss appearance between real objects and their visualization on a display device.},
author = {Chen, Bin and Piovarci, Michael and Wang, Chao and Seidel, Hans-Peter and Didyk, Piotr and Myszkowski, Karol and Serrano, Ana},
booktitle = {SIGGRAPH Asia 2022 Conference Papers},
isbn = {9781450394703},
location = {Daegu, South Korea},
publisher = {Association for Computing Machinery},
title = {{Gloss management for consistent reproduction of real and virtual objects}},
doi = {10.1145/3550469.3555406},
volume = {2022},
year = {2022},
}
@inproceedings{12452,
abstract = {Portrait viewpoint and illumination editing is an important problem with several applications in VR/AR, movies, and photography. Comprehensive knowledge of geometry and illumination is critical for obtaining photorealistic results. Current methods are unable to explicitly model in 3D while handing both viewpoint and illumination editing from a single image. In this paper, we propose VoRF, a novel approach that can take even a single portrait image as input and relight human heads under novel illuminations that can be viewed from arbitrary viewpoints. VoRF represents a human head as a continuous volumetric field and learns a prior model of human heads using a coordinate-based MLP with separate latent spaces for identity and illumination. The prior model is learnt in an auto-decoder manner over a diverse class of head shapes and appearances, allowing VoRF to generalize to novel test identities from a single input image. Additionally, VoRF has a reflectance MLP that uses the intermediate features of the prior model for rendering One-Light-at-A-Time (OLAT) images under novel views. We synthesize novel illuminations by combining these OLAT images with target environment maps. Qualitative and quantitative evaluations demonstrate the effectiveness of VoRF for relighting and novel view synthesis even when applied to unseen subjects under uncontrolled illuminations.},
author = {Rao, Pramod and B R, Mallikarjun and Fox, Gereon and Weyrich, Tim and Bickel, Bernd and Seidel, Hans-Peter and Pfister, Hanspeter and Matusik, Wojciech and Tewari, Ayush and Theobalt, Christian and Elgharib, Mohamed},
booktitle = {33rd British Machine Vision Conference},
location = {London, United Kingdom},
publisher = {British Machine Vision Association and Society for Pattern Recognition},
title = {{VoRF: Volumetric Relightable Faces}},
year = {2022},
}
@article{10922,
abstract = {We study structural rigidity for assemblies with mechanical joints. Existing methods identify whether an assembly is structurally rigid by assuming parts are perfectly rigid. Yet, an assembly identified as rigid may not be that “rigid” in practice, and existing methods cannot quantify how rigid an assembly is. We address this limitation by developing a new measure, worst-case rigidity, to quantify the rigidity of an assembly as the largest possible deformation that the assembly undergoes for arbitrary external loads of fixed magnitude. Computing worst-case rigidity is non-trivial due to non-rigid parts and different joint types. We thus formulate a new computational approach by encoding parts and their connections into a stiffness matrix, in which parts are modeled as deformable objects and joints as soft constraints. Based on this, we formulate worst-case rigidity analysis as an optimization that seeks the worst-case deformation of an assembly for arbitrary external loads, and solve the optimization problem via an eigenanalysis. Furthermore, we present methods to optimize the geometry and topology of various assemblies to enhance their rigidity, as guided by our rigidity measure. In the end, we validate our method on a variety of assembly structures with physical experiments and demonstrate its effectiveness by designing and fabricating several structurally rigid assemblies.},
author = {Liu, Zhenyuan and Hu, Jingyu and Xu, Hao and Song, Peng and Zhang, Ran and Bickel, Bernd and Fu, Chi-Wing},
issn = {1467-8659},
journal = {Computer Graphics Forum},
number = {2},
pages = {507--519},
publisher = {Wiley},
title = {{Worst-case rigidity analysis and optimization for assemblies with mechanical joints}},
doi = {10.1111/cgf.14490},
volume = {41},
year = {2022},
}
@article{17065,
abstract = {Past work on optimizing fabrication plans given a carpentry design can provide Pareto-optimal plans trading off between material waste, fabrication time, precision, and other considerations. However, when developing fabrication plans, experts rarely restrict to a single design, instead considering families of design variations, sometimes adjusting designs to simplify fabrication. Jointly exploring the design and fabrication plan spaces for each design is intractable using current techniques. We present a new approach to jointly optimize design and fabrication plans for carpentered objects. To make this bi-level optimization tractable, we adapt recent work from program synthesis based on equality graphs (e-graphs), which encode sets of equivalent programs. Our insight is that subproblems within our bi-level problem share significant substructures. By representing both designs and fabrication plans in a new bag of parts (BOP) e-graph, we amortize the cost of optimizing design components shared among multiple candidates. Even using BOP e-graphs, the optimization space grows quickly in practice. Hence, we also show how a feedback-guided search strategy dubbed Iterative Contraction and Expansion on E-graphs (ICEE) can keep the size of the e-graph manageable and direct the search towards promising candidates. We illustrate the advantages of our pipeline through examples from the carpentry domain.},
author = {Zhao, Haisen and Willsey, Max and Zhu, Amy and Nandi, Chandrakana and Tatlock, Zachary and Solomon, Justin and Schulz, Adriana},
issn = {1557-7368},
journal = {ACM Transactions on Graphics},
number = {3},
publisher = {Association for Computing Machinery},
title = {{Co-optimization of design and fabrication plans for carpentry}},
doi = {10.1145/3508499},
volume = {41},
year = {2022},
}
@unpublished{11943,
abstract = {Complex wiring between neurons underlies the information-processing network enabling all brain functions, including cognition and memory. For understanding how the network is structured, processes information, and changes over time, comprehensive visualization of the architecture of living brain tissue with its cellular and molecular components would open up major opportunities. However, electron microscopy (EM) provides nanometre-scale resolution required for full in-silico reconstruction1–5, yet is limited to fixed specimens and static representations. Light microscopy allows live observation, with super-resolution approaches6–12 facilitating nanoscale visualization, but comprehensive 3D-reconstruction of living brain tissue has been hindered by tissue photo-burden, photobleaching, insufficient 3D-resolution, and inadequate signal-to-noise ratio (SNR). Here we demonstrate saturated reconstruction of living brain tissue. We developed an integrated imaging and analysis technology, adapting stimulated emission depletion (STED) microscopy6,13 in extracellularly labelled tissue14 for high SNR and near-isotropic resolution. Centrally, a two-stage deep-learning approach leveraged previously obtained information on sample structure to drastically reduce photo-burden and enable automated volumetric reconstruction down to single synapse level. Live reconstruction provides unbiased analysis of tissue architecture across time in relation to functional activity and targeted activation, and contextual understanding of molecular labelling. This adoptable technology will facilitate novel insights into the dynamic functional architecture of living brain tissue.},
author = {Velicky, Philipp and Miguel Villalba, Eder and Michalska, Julia M and Wei, Donglai and Lin, Zudi and Watson, Jake and Troidl, Jakob and Beyer, Johanna and Ben Simon, Yoav and Sommer, Christoph M and Jahr, Wiebke and Cenameri, Alban and Broichhagen, Johannes and Grant, Seth G. N. and Jonas, Peter M and Novarino, Gaia and Pfister, Hanspeter and Bickel, Bernd and Danzl, Johann G},
booktitle = {bioRxiv},
publisher = {Cold Spring Harbor Laboratory},
title = {{Saturated reconstruction of living brain tissue}},
doi = {10.1101/2022.03.16.484431},
year = {2022},
}
@article{10574,
abstract = {The understanding of material appearance perception is a complex problem due to interactions between material reflectance, surface geometry, and illumination. Recently, Serrano et al. collected the largest dataset to date with subjective ratings of material appearance attributes, including glossiness, metallicness, sharpness and contrast of reflections. In this work, we make use of their dataset to investigate for the first time the impact of the interactions between illumination, geometry, and eight different material categories in perceived appearance attributes. After an initial analysis, we select for further analysis the four material categories that cover the largest range for all perceptual attributes: fabric, plastic, ceramic, and metal. Using a cumulative link mixed model (CLMM) for robust regression, we discover interactions between these material categories and four representative illuminations and object geometries. We believe that our findings contribute to expanding the knowledge on material appearance perception and can be useful for many applications, such as scene design, where any particular material in a given shape can be aligned with dominant classes of illumination, so that a desired strength of appearance attributes can be achieved.},
author = {Chen, Bin and Wang, Chao and Piovarci, Michael and Seidel, Hans Peter and Didyk, Piotr and Myszkowski, Karol and Serrano, Ana},
issn = {1432-2315},
journal = {Visual Computer},
number = {12},
pages = {2975--2987},
publisher = {Springer Nature},
title = {{The effect of geometry and illumination on appearance perception of different material categories}},
doi = {10.1007/s00371-021-02227-x},
volume = {37},
year = {2021},
}
@inproceedings{10148,
abstract = {Tactile feedback of an object’s surface enables us to discern its material properties and affordances. This understanding is used in digital fabrication processes by creating objects with high-resolution surface variations to influence a user’s tactile perception. As the design of such surface haptics commonly relies on knowledge from real-life experiences, it is unclear how to adapt this information for digital design methods. In this work, we investigate replicating the haptics of real materials. Using an existing process for capturing an object’s microgeometry, we digitize and reproduce the stable surface information of a set of 15 fabric samples. In a psychophysical experiment, we evaluate the tactile qualities of our set of original samples and their replicas. From our results, we see that direct reproduction of surface variations is able to influence different psychophysical dimensions of the tactile perception of surface textures. While the fabrication process did not preserve all properties, our approach underlines that replication of surface microgeometries benefits fabrication methods in terms of haptic perception by covering a large range of tactile variations. Moreover, by changing the surface structure of a single fabricated material, its material perception can be influenced. We conclude by proposing strategies for capturing and reproducing digitized textures to better resemble the perceived haptics of the originals.},
author = {Degraen, Donald and Piovarci, Michael and Bickel, Bernd and Kruger, Antonio},
booktitle = {34th Annual ACM Symposium},
isbn = {978-1-4503-8635-7},
location = {Virtual},
pages = {954--971},
publisher = {Association for Computing Machinery},
title = {{Capturing tactile properties of real surfaces for haptic reproduction}},
doi = {10.1145/3472749.3474798},
year = {2021},
}
@article{10184,
abstract = {We introduce a novel technique to automatically decompose an input object’s volume into a set of parts that can be represented by two opposite height fields. Such decomposition enables the manufacturing of individual parts using two-piece reusable rigid molds. Our decomposition strategy relies on a new energy formulation that utilizes a pre-computed signal on the mesh volume representing the accessibility for a predefined set of extraction directions. Thanks to this novel formulation, our method allows for efficient optimization of a fabrication-aware partitioning of volumes in a completely
automatic way. We demonstrate the efficacy of our approach by generating valid volume partitionings for a wide range of complex objects and physically reproducing several of them.},
author = {Alderighi, Thomas and Malomo, Luigi and Bickel, Bernd and Cignoni, Paolo and Pietroni, Nico},
issn = {1557-7368 },
journal = {ACM Transactions on Graphics},
number = {6},
publisher = {Association for Computing Machinery},
title = {{Volume decomposition for two-piece rigid casting}},
doi = {10.1145/3478513.3480555},
volume = {40},
year = {2021},
}
@article{9241,
abstract = {Volumetric light transport is a pervasive physical phenomenon, and therefore its accurate simulation is important for a broad array of disciplines. While suitable mathematical models for computing the transport are now available, obtaining the necessary material parameters needed to drive such simulations is a challenging task: direct measurements of these parameters from material samples are seldom possible. Building on the inverse scattering paradigm, we present a novel measurement approach which indirectly infers the transport parameters from extrinsic observations of multiple-scattered radiance. The novelty of the proposed approach lies in replacing structured illumination with a structured reflector bonded to the sample, and a robust fitting procedure that largely compensates for potential systematic errors in the calibration of the setup. We show the feasibility of our approach by validating simulations of complex 3D compositions of the measured materials against physical prints, using photo-polymer resins. As presented in this paper, our technique yields colorspace data suitable for accurate appearance reproduction in the area of 3D printing. Beyond that, and without fundamental changes to the basic measurement methodology, it could equally well be used to obtain spectral measurements that are useful for other application areas.},
author = {Elek, Oskar and Zhang, Ran and Sumin, Denis and Myszkowski, Karol and Bickel, Bernd and Wilkie, Alexander and Křivánek, Jaroslav and Weyrich, Tim},
issn = {1094-4087},
journal = {Optics Express},
number = {5},
pages = {7568--7588},
publisher = {The Optical Society},
title = {{Robust and practical measurement of volume transport parameters in solid photo-polymer materials for 3D printing}},
doi = {10.1364/OE.406095},
volume = {29},
year = {2021},
}
@article{9376,
abstract = {This paper presents a method for designing planar multistable compliant structures. Given a sequence of desired stable states and the corresponding poses of the structure, we identify the topology and geometric realization of a mechanism—consisting of bars and joints—that is able to physically reproduce the desired multistable behavior. In order to solve this problem efficiently, we build on insights from minimally rigid graph theory to identify simple but effective topologies for the mechanism. We then optimize its geometric parameters, such as joint positions and bar lengths, to obtain correct transitions between the given poses. Simultaneously, we ensure adequate stability of each pose based on an effective approximate error metric related to the elastic energy Hessian of the bars in the mechanism. As demonstrated by our results, we obtain functional multistable mechanisms of manageable complexity that can be fabricated using 3D printing. Further, we evaluated the effectiveness of our method on a large number of examples in the simulation and fabricated several physical prototypes.},
author = {Zhang, Ran and Auzinger, Thomas and Bickel, Bernd},
issn = {1557-7368},
journal = {ACM Transactions on Graphics},
keywords = {multistability, mechanism, computational design, rigidity},
number = {5},
publisher = {Association for Computing Machinery},
title = {{Computational design of planar multistable compliant structures}},
doi = {10.1145/3453477},
volume = {40},
year = {2021},
}
@article{9408,
abstract = {We present a computational design system that assists users to model, optimize, and fabricate quad-robots with soft skins. Our system addresses the challenging task of predicting their physical behavior by fully integrating the multibody dynamics of the mechanical skeleton and the elastic behavior of the soft skin. The developed motion control strategy uses an alternating optimization scheme to avoid expensive full space time-optimization, interleaving space-time optimization for the skeleton, and frame-by-frame optimization for the full dynamics. The output are motor torques to drive the robot to achieve a user prescribed motion trajectory. We also provide a collection of convenient engineering tools and empirical manufacturing guidance to support the fabrication of the designed quad-robot. We validate the feasibility of designs generated with our system through physics simulations and with a physically-fabricated prototype.},
author = {Feng, Xudong and Liu, Jiafeng and Wang, Huamin and Yang, Yin and Bao, Hujun and Bickel, Bernd and Xu, Weiwei},
issn = {1077-2626},
journal = {IEEE Transactions on Visualization and Computer Graphics},
number = {6},
publisher = {IEEE},
title = {{Computational design of skinned Quad-Robots}},
doi = {10.1109/TVCG.2019.2957218},
volume = {27},
year = {2021},
}
@article{9547,
abstract = {With the wider availability of full-color 3D printers, color-accurate 3D-print preparation has received increased attention. A key challenge lies in the inherent translucency of commonly used print materials that blurs out details of the color texture. Previous work tries to compensate for these scattering effects through strategic assignment of colored primary materials to printer voxels. To date, the highest-quality approach uses iterative optimization that relies on computationally expensive Monte Carlo light transport simulation to predict the surface appearance from subsurface scattering within a given print material distribution; that optimization, however, takes in the order of days on a single machine. In our work, we dramatically speed up the process by replacing the light transport simulation with a data-driven approach. Leveraging a deep neural network to predict the scattering within a highly heterogeneous medium, our method performs around two orders of magnitude faster than Monte Carlo rendering while yielding optimization results of similar quality level. The network is based on an established method from atmospheric cloud rendering, adapted to our domain and extended by a physically motivated weight sharing scheme that substantially reduces the network size. We analyze its performance in an end-to-end print preparation pipeline and compare quality and runtime to alternative approaches, and demonstrate its generalization to unseen geometry and material values. This for the first time enables full heterogenous material optimization for 3D-print preparation within time frames in the order of the actual printing time.},
author = {Rittig, Tobias and Sumin, Denis and Babaei, Vahid and Didyk, Piotr and Voloboy, Alexey and Wilkie, Alexander and Bickel, Bernd and Myszkowski, Karol and Weyrich, Tim and Křivánek, Jaroslav},
issn = {1467-8659},
journal = {Computer Graphics Forum},
number = {2},
pages = {205--219},
publisher = {Wiley},
title = {{Neural acceleration of scattering-aware color 3D printing}},
doi = {10.1111/cgf.142626},
volume = {40},
year = {2021},
}
@inproceedings{9957,
abstract = {The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing. Most existing methods for estimating the face reflectance from a monocular image assume faces to be diffuse with very few approaches adding a specular component. This still leaves out important perceptual aspects of reflectance as higher-order global illumination effects and self-shadowing are not modeled. We present a new neural representation for face reflectance where we can estimate all components of the reflectance responsible for the final appearance from a single monocular image. Instead of modeling each component of the reflectance separately using parametric models, our neural representation allows us to generate a basis set of faces in a geometric deformation-invariant space, parameterized by the input light direction, viewpoint and face geometry. We learn to reconstruct this reflectance field of a face just from a monocular image, which can be used to render the face from any viewpoint in any light condition. Our method is trained on a light-stage training dataset, which captures 300 people illuminated with 150 light conditions from 8 viewpoints. We show that our method outperforms existing monocular reflectance reconstruction methods, in terms of photorealism due to better capturing of physical premitives, such as sub-surface scattering, specularities, self-shadows and other higher-order effects.},
author = {B R, Mallikarjun and Tewari, Ayush and Oh, Tae-Hyun and Weyrich, Tim and Bickel, Bernd and Seidel, Hans-Peter and Pfister, Hanspeter and Matusik, Wojciech and Elgharib, Mohamed and Theobalt, Christian},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
isbn = {978-166544509-2},
issn = {1063-6919},
location = {Nashville, TN, United States; Virtual},
pages = {4791--4800},
publisher = {IEEE},
title = {{Monocular reconstruction of neural face reflectance fields}},
doi = {10.1109/CVPR46437.2021.00476},
year = {2021},
}
@article{9819,
abstract = {Photorealistic editing of head portraits is a challenging task as humans are very sensitive to inconsistencies in faces. We present an approach for high-quality intuitive editing of the camera viewpoint and scene illumination (parameterised with an environment map) in a portrait image. This requires our method to capture and control the full reflectance field of the person in the image. Most editing approaches rely on supervised learning using training data captured with setups such as light and camera stages. Such datasets are expensive to acquire, not readily available and do not capture all the rich variations of in-the-wild portrait images. In addition, most supervised approaches only focus on relighting, and do not allow camera viewpoint editing. Thus, they only capture and control a subset of the reflectance field. Recently, portrait editing has been demonstrated by operating in the generative model space of StyleGAN. While such approaches do not require direct supervision, there is a significant loss of quality when compared to the supervised approaches. In this paper, we present a method which learns from limited supervised training data. The training images only include people in a fixed neutral expression with eyes closed, without much hair or background variations. Each person is captured under 150 one-light-at-a-time conditions and under 8 camera poses. Instead of training directly in the image space, we design a supervised problem which learns transformations in the latent space of StyleGAN. This combines the best of supervised learning and generative adversarial modeling. We show that the StyleGAN prior allows for generalisation to different expressions, hairstyles and backgrounds. This produces high-quality photorealistic results for in-the-wild images and significantly outperforms existing methods. Our approach can edit the illumination and pose simultaneously, and runs at interactive rates.},
author = {Mallikarjun, B. R. and Tewari, Ayush and Dib, Abdallah and Weyrich, Tim and Bickel, Bernd and Seidel, Hans Peter and Pfister, Hanspeter and Matusik, Wojciech and Chevallier, Louis and Elgharib, Mohamed A. and Theobalt, Christian},
issn = {1557-7368},
journal = {ACM Transactions on Graphics},
number = {4},
publisher = {Association for Computing Machinery},
title = {{PhotoApp: Photorealistic appearance editing of head portraits}},
doi = {10.1145/3450626.3459765},
volume = {40},
year = {2021},
}
@article{9820,
abstract = {Material appearance hinges on material reflectance properties but also surface geometry and illumination. The unlimited number of potential combinations between these factors makes understanding and predicting material appearance a very challenging task. In this work, we collect a large-scale dataset of perceptual ratings of appearance attributes with more than 215,680 responses for 42,120 distinct combinations of material, shape, and illumination. The goal of this dataset is twofold. First, we analyze for the first time the effects of illumination and geometry in material perception across such a large collection of varied appearances. We connect our findings to those of the literature, discussing how previous knowledge generalizes across very diverse materials, shapes, and illuminations. Second, we use the collected dataset to train a deep learning architecture for predicting perceptual attributes that correlate with human judgments. We demonstrate the consistent and robust behavior of our predictor in various challenging scenarios, which, for the first time, enables estimating perceived material attributes from general 2D images. Since our predictor relies on the final appearance in an image, it can compare appearance properties across different geometries and illumination conditions. Finally, we demonstrate several applications that use our predictor, including appearance reproduction using 3D printing, BRDF editing by integrating our predictor in a differentiable renderer, illumination design, or material recommendations for scene design.},
author = {Serrano, Ana and Chen, Bin and Wang, Chao and Piovarci, Michael and Seidel, Hans Peter and Didyk, Piotr and Myszkowski, Karol},
issn = {1557-7368},
journal = {ACM Transactions on Graphics},
number = {4},
publisher = {Association for Computing Machinery},
title = {{The effect of shape and illumination on material perception: Model and applications}},
doi = {10.1145/3450626.3459813},
volume = {40},
year = {2021},
}
@article{9817,
abstract = {Elastic bending of initially flat slender elements allows the realization and economic fabrication of intriguing curved shapes. In this work, we derive an intuitive but rigorous geometric characterization of the design space of plane elastic rods with variable stiffness. It enables designers to determine which shapes are physically viable with active bending by visual inspection alone. Building on these insights, we propose a method for efficiently designing the geometry of a flat elastic rod that realizes a target equilibrium curve, which only requires solving a linear program. We implement this method in an interactive computational design tool that gives feedback about the feasibility of a design, and computes the geometry of the structural elements necessary to realize it within an instant. The tool also offers an iterative optimization routine that improves the fabricability of a model while modifying it as little as possible. In addition, we use our geometric characterization to derive an algorithm for analyzing and recovering the stability of elastic curves that would otherwise snap out of their unstable equilibrium shapes by buckling. We show the efficacy of our approach by designing and manufacturing several physical models that are assembled from flat elements.},
author = {Hafner, Christian and Bickel, Bernd},
issn = {1557-7368},
journal = {ACM Transactions on Graphics},
keywords = {Computing methodologies, shape modeling, modeling and simulation, theory of computation, computational geometry, mathematics of computing, mathematical optimization},
location = {Virtual},
number = {4},
publisher = {Association for Computing Machinery},
title = {{The design space of plane elastic curves}},
doi = {10.1145/3450626.3459800},
volume = {40},
year = {2021},
}
@misc{8761,
author = {Guseinov, Ruslan},
publisher = {Institute of Science and Technology Austria},
title = {{Supplementary data for "Computational design of cold bent glass façades"}},
doi = {10.15479/AT:ISTA:8761},
year = {2020},
}
@article{8766,
abstract = {The “procedural” approach to animating ocean waves is the dominant algorithm for animating larger bodies of water in
interactive applications as well as in off-line productions — it provides high visual quality with a low computational demand. In this paper, we widen the applicability of procedural water wave animation with an extension that guarantees the satisfaction of boundary conditions imposed by terrain while still approximating physical wave behavior. In combination with a particle system that models wave breaking, foam, and spray, this allows us to naturally model waves interacting with beaches and rocks. Our system is able to animate waves at large scales at interactive frame rates on a commodity PC.},
author = {Jeschke, Stefan and Hafner, Christian and Chentanez, Nuttapong and Macklin, Miles and Müller-Fischer, Matthias and Wojtan, Christopher J},
journal = {Computer Graphics forum},
location = {Online Symposium},
number = {8},
pages = {47--54},
publisher = {Wiley},
title = {{Making procedural water waves boundary-aware}},
doi = {10.1111/cgf.14100},
volume = {39},
year = {2020},
}