Browsing by Author "Weyrich, Tim"

Now showing 1 - 10 of 10
  • Thumbnail Image
    Item
    Co-developing Knowledge Documentation for the Intangible Heritage of Egyptian Woodwork Craft
    (The Eurographics Association, 2022) Samaroudi, Myrsini; Rodriguez Echavarria, Karina; Amis, Tim; Sharara, Nesreen; Ali, Noha; Aboulfadl, Abdelrahman; Salah, AbdelHamid; Abdel Barr, Omniya; Weyrich, Tim; Ponchio, Federico; Pintus, Ruggero
    This research contributes towards the need to decolonise material culture knowledge by reaching out to communities across the world who still practice the intangible heritage of craft and linking their knowledge with the historical collections curated and exhibited in western heritage institutions. Craft know-how has been transmitted from past generations and in many instances still plays a key role in the economic development and social welfare within communities. Such development includes creative and handicraft industries which are under threat by mass production and the loss of traditional know-how. For western museums, the documentation of knowledge around craft can enhance our understanding and interpretation of collections. For communities, there is a potential to support preserving their endangered knowledge while offering opportunities to seek innovation through the digital transformation of their practices to benefit at a financial and socioeconomic level. This paper describes an ongoing research project which deploys visual methods and linked data to document and provide access to the intangible knowledge of the craft, which is practised by Egyptian woodwork crafters in the historic centre of Cairo.
  • Thumbnail Image
    Item
    Eurographics Workshop on Graphics and Cultural Heritage (GCH) 2017: Frontmatter
    (Eurographics Association, 2017) Schreck, Tobias; Weyrich, Tim; Sablatnig, Robert; Štular, Benjamin; Tobias Schreck and Tim Weyrich and Robert Sablatnig and Benjamin Stular
  • No Thumbnail Available
    Item
    Exploring Expert and Non-Expert Perception of 3D Digital Models of Museum Objects
    (The Eurographics Association, 2023) Zumkley, Kira; Echavarria, Karina Rodriguez; Weyrich, Tim; Bucciero, Alberto; Fanini, Bruno; Graf, Holger; Pescarin, Sofia; Rizvic, Selma
    Increasingly, museum objects are documented as 3D digital models (3dDM) for scientific study, online exhibition, or personal enjoyment; however, 3dDMs invariably exhibit imperfections due to technological limitations and/or the lack of standardisation in museum object digitisation. Little is known how such inaccuracies are perceived and interpreted by users. Through qualitative interviews and deductive thematic analysis this user study first investigates which inaccuracies in 3dDMs lead to misinterpretations by users and then considers six factors based on the concept of Epistemic Vigilance (EV) and to what extend these factors play a role in the users' ability to correctly understand the information presented within 3dDMs. Only one of eight explored inaccuracies was correctly identified by all participants and background knowledge of the museum object and 3D imaging technology (3DIT) had the most influence on correct interpretation of inaccuracies. Furthermore, trust in the museum publishing the 3dDM and in 3DIT also played a role in how the inaccuracies were perceived. Publishing data about the issues present alongside the 3dDM will increase transparency and further work should therefore concentrate on mechanisms that promote correct interpretation of 3dDMs' limitations to enable museum practitioners to make the most of their digitisation efforts.
  • Thumbnail Image
    Item
    Interactive 3D Artefact Puzzles to Support Engagement Beyond the Museum Environment
    (The Eurographics Association, 2021) Rodriguez Echavarria, Karina; Samaroudi, Myrsini; LLoyd, Jack; Weyrich, Tim; Hulusic, Vedad and Chalmers, Alan
    The need for online 3D interactive experiences was evidenced during the COVID-19 lockdowns, as audiences across the world have been unable to visit museums, physically interact with their collections on site or digitally interact with technologies and digital media situated within such settings. As a response, this research addresses gaps identified in a review of the digital offerings from UK and US museums during the 2020 lockdowns, highlighting the limited number and nature of 3D interactive offerings provided, despite the wide efforts on 3D digitisation over the last decade. Thus, the research investigates the development and testing of an online 3D interactive activity, resembling a physical activity situated in the archaeological gallery of Brighton Museum and Art Gallery (UK). Through a pilot user survey, the research aims to understand what is the impact of such online offerings to better contextualise heritage collections; enhance cultural heritage learning and appreciation; and complement physical activities of similar nature. The analysis of audiences’' opinions about these interactions can be of great importance, as such activities have the power to enable active access to cultural heritage resources regardless of the physical location of users and transform heritage experiences in the long term. Our research indicates that, while the physical experience might offer advantages as far as it concerns the familiarity with the tactile nature of interaction, the digital counterpart has potential to allow for the experience of assembling the puzzle to achieve a wider reach.
  • Thumbnail Image
    Item
    Neural Acceleration of Scattering-Aware Color 3D Printing
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Rittig, Tobias; Sumin, Denis; Babaei, Vahid; Didyk, Piotr; Voloboy, Alexey; Wilkie, Alexander; Bickel, Bernd; Myszkowski, Karol; Weyrich, Tim; Krivánek, Jaroslav; Mitra, Niloy and Viola, Ivan
    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.
  • Thumbnail Image
    Item
    Neural BRDF Representation and Importance Sampling
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Sztrajman, Alejandro; Rainer, Gilles; Ritschel, Tobias; Weyrich, Tim; Benes, Bedrich and Hauser, Helwig
    Controlled capture of real‐world material appearance yields tabulated sets of highly realistic reflectance data. In practice, however, its high memory footprint requires compressing into a representation that can be used efficiently in rendering while remaining faithful to the original. Previous works in appearance encoding often prioritized one of these requirements at the expense of the other, by either applying high‐fidelity array compression strategies not suited for efficient queries during rendering, or by fitting a compact analytic model that lacks expressiveness. We present a compact neural network‐based representation of BRDF data that combines high‐accuracy reconstruction with efficient practical rendering via built‐in interpolation of reflectance. We encode BRDFs as lightweight networks, and propose a training scheme with adaptive angular sampling, critical for the accurate reconstruction of specular highlights. Additionally, we propose a novel approach to make our representation amenable to importance sampling: rather than inverting the trained networks, we learn to encode them in a more compact embedding that can be mapped to parameters of an analytic BRDF for which importance sampling is known. We evaluate encoding results on isotropic and anisotropic BRDFs from multiple real‐world datasets, and importance sampling performance for isotropic BRDFs mapped to two different analytic models.
  • No Thumbnail Available
    Item
    Neural BTF Compression and Interpolation
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Rainer, Gilles; Jakob, Wenzel; Ghosh, Abhijeet; Weyrich, Tim; Alliez, Pierre and Pellacini, Fabio
    The Bidirectional Texture Function (BTF) is a data-driven solution to render materials with complex appearance. A typical capture contains tens of thousands of images of a material sample under varying viewing and lighting conditions.While capable of faithfully recording complex light interactions in the material, the main drawback is the massive memory requirement, both for storing and rendering, making effective compression of BTF data a critical component in practical applications. Common compression schemes used in practice are based on matrix factorization techniques, which preserve the discrete format of the original dataset. While this approach generalizes well to different materials, rendering with the compressed dataset still relies on interpolating between the closest samples. Depending on the material and the angular resolution of the BTF, this can lead to blurring and ghosting artefacts. An alternative approach uses analytic model fitting to approximate the BTF data, using continuous functions that naturally interpolate well, but whose expressive range is often not wide enough to faithfully recreate materials with complex non-local lighting effects (subsurface scattering, inter-reflections, shadowing and masking...). In light of these observations, we propose a neural network-based BTF representation inspired by autoencoders: our encoder compresses each texel to a small set of latent coefficients, while our decoder additionally takes in a light and view direction and outputs a single RGB vector at a time. This allows us to continuously query reflectance values in the light and view hemispheres, eliminating the need for linear interpolation between discrete samples. We train our architecture on fabric BTFs with a challenging appearance and compare to standard PCA as a baseline. We achieve competitive compression ratios and high-quality interpolation/extrapolation without blurring or ghosting artifacts.
  • Thumbnail Image
    Item
    Preserving Ceramic Industrial Heritage Through Digital Technologies
    (The Eurographics Association, 2019) Echavarria, Karina Rodriguez; Weyrich, Tim; Brownsword, Neil; Rizvic, Selma and Rodriguez Echavarria, Karina
    World-renowned for its perfection of Bone China and underglaze blue printing techniques, the historic Spode Works in Stokeon- Trent was one of the few ceramic factories in Britain to have operated continuously on its original site until the company ceased trading in 2008. Since then the site has undergone many transitions with much of its former production infrastructure being discarded. Currently the site holds an estimated 70,000 moulds once used in ceramic production dating from the mid 19th century to 2008, which remain as critical elements of British industrial history at risk of disappearing. This paper presents on-going research which explores the application of 3D technologies to create digital surrogates to support the preservation of these Cultural Heritage artefacts, and ways through which their form and context can be explored to creatively disseminate the associated histories of their production. Given the complex nature of ceramic manufacturing as well as the large-scale of the problem, this is not an easy challenge. Hence, the research investigates workflows and technologies which can support creating a digital, and potentially physical, archive with a selection of mould typologies, shapes and complexities. To further understand the complexities of industrial craft practices, the resultant dataset also aims to elucidate material and craft knowledge embodied within such objects. For this, the research looks into novel manufacturing processes, such as 3D printing, to re-invent the physical shapes documented in these moulds in new interpretations of this historic legacy.
  • Thumbnail Image
    Item
    Tactile prints in colour: Studying the Visual Appearance of 2.5D Prints for Heritage Recreations
    (The Eurographics Association, 2022) Trujillo-Vazquez, Abigail; Rodriguez Echavarria, Karina; Weyrich, Tim; Ponchio, Federico; Pintus, Ruggero
    Printing applications for heritage recreation are a means to allow audiences to appreciate details and engage with cultural materials through closer interaction. A 2.5D print is a media suitable to incorporate visual and tactile qualities such as colour, low relief, textures and roughness. Designing a colour-accurate tactile print requires, nevertheless, anticipating how specific shapes and meso-geometries will affect the reflective properties of the surface, thus changing its appearance. Hence, this paper contributes to improve the understanding of the interaction between geometry and colour when deploying 2.5D prints so that tactile portable replicas can be easily produced. For this, we have produced a series of 2.5D printed patches with varying meso-textures, based on procedural noise functions, and measured their colour coordinates and glossiness. We aim to find a correlation between colour shift (expressed as lightness, chroma and ?E) and the scale and distribution of surface details.
  • Thumbnail Image
    Item
    Unified Neural Encoding of BTFs
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Rainer, Gilles; Ghosh, Abhijeet; Jakob, Wenzel; Weyrich, Tim; Panozzo, Daniele and Assarsson, Ulf
    Realistic rendering using discrete reflectance measurements is challenging, because arbitrary directions on the light and view hemispheres are queried at render time, incurring large memory requirements and the need for interpolation. This explains the desire for compact and continuously parametrized models akin to analytic BRDFs; however, fitting BRDF parameters to complex data such as BTF texels can prove challenging, as models tend to describe restricted function spaces that cannot encompass real-world behavior. Recent advances in this area have increasingly relied on neural representations that are trained to reproduce acquired reflectance data. The associated training process is extremely costly and must typically be repeated for each material. Inspired by autoencoders, we propose a unified network architecture that is trained on a variety of materials, and which projects reflectance measurements to a shared latent parameter space. Similarly to SVBRDF fitting, real-world materials are represented by parameter maps, and the decoder network is analog to the analytic BRDF expression (also parametrized on light and view directions for practical rendering application). With this approach, encoding and decoding materials becomes a simple matter of evaluating the network. We train and validate on BTF datasets of the University of Bonn, but there are no prerequisites on either the number of angular reflectance samples, or the sample positions. Additionally, we show that the latent space is well-behaved and can be sampled from, for applications such as mipmapping and texture synthesis.