{"title":"Surface and Edge Detection for Primitive Fitting of Point Clouds","authors":"Yuanqi Li, Shun Liu, Xinran Yang, Jianwei Guo, Jie Guo, Yanwen Guo","doi":"10.1145/3588432.3591522","DOIUrl":"https://doi.org/10.1145/3588432.3591522","url":null,"abstract":"Fitting primitives for point cloud data to obtain a structural representation has been widely adopted for reverse engineering and other graphics applications. Existing segmentation-based approaches only segment primitive patches but ignore edges that indicate boundaries of primitives, leading to inaccurate and incomplete reconstruction. To fill the gap, we present a novel surface and edge detection network (SED-Net) for accurate geometric primitive fitting of point clouds. The key idea is to learn parametric surfaces (including B-spline patches) and edges jointly that can be assembled into a regularized and seamless CAD model in one unified and efficient framework. SED-Net is equipped with a two-branch structure to extract type and edge features and geometry features of primitives. At the core of our network is a two-stage feature fusion mechanism to utilize the type, edge and geometry features fully. Precisely detected surface patches can be employed as contextual information to facilitate the detection of edges and corners. Benefiting from the simultaneous detection of surfaces and edges, we can obtain a parametric and compact model representation. This enables us to represent a CAD model with predefined primitive-specific meshes and also allows users to edit its shape easily. Extensive experiments and comparisons against previous methods demonstrate our effectiveness and superiority.","PeriodicalId":280036,"journal":{"name":"ACM SIGGRAPH 2023 Conference Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116741215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Martin Balint, Krzysztof Wolski, K. Myszkowski, H. Seidel, Rafał K. Mantiuk
{"title":"Neural Partitioning Pyramids for Denoising Monte Carlo Renderings","authors":"Martin Balint, Krzysztof Wolski, K. Myszkowski, H. Seidel, Rafał K. Mantiuk","doi":"10.1145/3588432.3591562","DOIUrl":"https://doi.org/10.1145/3588432.3591562","url":null,"abstract":"Recent advancements in hardware-accelerated raytracing made it possible to achieve interactive framerates even for algorithms previously considered offline, such as path tracing. Interactive path tracing pipelines rely heavily on spatiotemporal denoising to produce a high-quality output from low-sample-count renderings. Such denoising is typically implemented as multiscale-kernel-based filters driven by lightweight U-Nets operating on pixels, and encoders operating on samples. In this work, we present a novel kernel architecture in the line of low-pass pyramid filters. Our architecture avoids the issues with the low-frequency response of previous such filters, resolving ringing, blotchiness, and box-shaped artefacts while improving overall detail. Instead of using classical downsampling and upsampling approaches, which are prone to aliasing, we let our weight predictor networks learn to partition the input radiance between pyramidal layers, predict kernels for denoising each partitioned and downscaled image, and then guide the upsampling process when combining layers. We present failure cases of pyramidal scale-composition in previous work and, through Fourier analysis, show how our method resolves them. Finally, we demonstrate state-of-the-art denoising performance.","PeriodicalId":280036,"journal":{"name":"ACM SIGGRAPH 2023 Conference Proceedings","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114112657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiahui Fan, Beibei Wang, Miloš Hašan, Jian Yang, Ling-Qi Yan
{"title":"Neural Biplane Representation for BTF Rendering and Acquisition","authors":"Jiahui Fan, Beibei Wang, Miloš Hašan, Jian Yang, Ling-Qi Yan","doi":"10.1145/3588432.3591505","DOIUrl":"https://doi.org/10.1145/3588432.3591505","url":null,"abstract":"Bidirectional Texture Functions (BTFs) are able to represent complex materials with greater generality than traditional analytical models. This holds true for both measured real materials and synthetic ones. Recent advancements in neural BTF representations have significantly reduced storage costs, making them more practical for use in rendering. These representations typically combine spatial feature (latent) textures with neural decoders that handle angular dimensions per spatial location. However, these models have yet to combine fast compression and inference, accuracy, and generality. In this paper, we propose a biplane representation for BTFs, which uses a feature texture in the half-vector domain as well as the spatial domain. This allows the learned representation to encode high-frequency details in both the spatial and angular domains. Our decoder is small yet general, meaning it is trained once and fixed. Additionally, we optionally combine this representation with a neural offset module for parallax and masking effects. Our model can represent a broad range of BTFs and has fast compression and inference due to its lightweight architecture. Furthermore, it enables a simple way to capture BTF data. By taking about 20 cell phone photos with a collocated camera and flash, our model can plausibly recover the entire BTF, despite never observing function values with differing view and light directions. We demonstrate the effectiveness of our model in the acquisition of many measured materials, including challenging materials such as fabrics.","PeriodicalId":280036,"journal":{"name":"ACM SIGGRAPH 2023 Conference Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123504542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Florine Hartwig, Josua Sassen, Omri Azencot, M. Rumpf, M. Ben-Chen
{"title":"An Elastic Basis for Spectral Shape Correspondence","authors":"Florine Hartwig, Josua Sassen, Omri Azencot, M. Rumpf, M. Ben-Chen","doi":"10.1145/3588432.3591518","DOIUrl":"https://doi.org/10.1145/3588432.3591518","url":null,"abstract":"Finding correspondences between shapes is a central task in geometry processing with applications such as texture or deformation transfer and shape interpolation. We develop a spectral method for finding correspondences between non-isometric shapes that aligns extrinsic features. For this, we propose a novel crease aware spectral basis, that is derived from the Hessian of an elastic thin shell energy. We incorporate this basis in a functional map framework and demonstrate the effectiveness of our approach for mapping non-isometric shapes such that prominent features are put in correspondence. Finally, we describe the necessary adaptations to the functional map framework for working with non-orthogonal basis functions, thus considerably widening the scope of future uses of spectral shape correspondence.","PeriodicalId":280036,"journal":{"name":"ACM SIGGRAPH 2023 Conference Proceedings","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121898465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zackary Misso, Y. Li, Brent Burley, Daniel Teece, Wojciech Jarosz
{"title":"Progressive null-tracking for volumetric rendering","authors":"Zackary Misso, Y. Li, Brent Burley, Daniel Teece, Wojciech Jarosz","doi":"10.1145/3588432.3591557","DOIUrl":"https://doi.org/10.1145/3588432.3591557","url":null,"abstract":"Null-collision approaches for estimating transmittance and sampling free-flight distances are the current state-of-the-art for unbiased rendering of general heterogeneous participating media. However, null-collision approaches have a strict requirement for specifying a tightly bounding total extinction in order to remain both robust and performant; in practice this requirement restricts the use of null-collision techniques to only participating media where the density of the medium at every possible point in space is known a-priori. In production rendering, a common case is a medium in which density is defined by a black-box procedural function for which a bounding extinction cannot be determined beforehand. Typically in this case, a bounding extinction must be approximated by using an overly loose and therefore computationally inefficient conservative estimate. We present an analysis of how null-collision techniques degrade when a more aggressive initial guess for a bounding extinction underestimates the true maximum density and turns out to be non-bounding. We then build upon this analysis to arrive at two new techniques: first, a practical, efficient, consistent progressive algorithm that allows us to robustly adapt null-collision techniques for use with procedural media with unknown bounding extinctions, and second, a new importance sampling technique that improves ratio-tracking based on zero-variance sampling.","PeriodicalId":280036,"journal":{"name":"ACM SIGGRAPH 2023 Conference Proceedings","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129638038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rahul Mitra, L. Makatura, E. Whiting, Edward Chien
{"title":"Helix-Free Stripes for Knit Graph Design","authors":"Rahul Mitra, L. Makatura, E. Whiting, Edward Chien","doi":"10.1145/3588432.3591564","DOIUrl":"https://doi.org/10.1145/3588432.3591564","url":null,"abstract":"The problem of placing evenly-spaced stripes on a triangular mesh mirrors that of having evenly-spaced course rows and wale columns in a knit graph for a given geometry. This work presents strategies for producing helix-free stripe patterns and traces them to produce helix-free knit graphs suitable for machine knitting. We optimize directly for the discrete differential (1-form) of the stripe texture function, i.e., the spinning form, and demonstrate the knitting-specific advantages of this framework. In particular, we note how simple linear constraints allow us to place stitch irregularities, align course rows and wale columns to boundary/feature curves, and eliminate helical stripes. Two mixed-integer optimization strategies using these constraints are presented and applied to several mesh models. The results are smooth, globally-informed, helix-free stripe patterns that we trace to produce machine-knittable graphs. We further provide an explicit characterization of helical stripes and a theoretical analysis of their elimination constraints.","PeriodicalId":280036,"journal":{"name":"ACM SIGGRAPH 2023 Conference Proceedings","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129941177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Junqiu Zhu, A. Jarabo, Carlos Aliaga, Ling-Qi Yan, M. Chiang
{"title":"A Realistic Surface-based Cloth Rendering Model","authors":"Junqiu Zhu, A. Jarabo, Carlos Aliaga, Ling-Qi Yan, M. Chiang","doi":"10.1145/3588432.3591554","DOIUrl":"https://doi.org/10.1145/3588432.3591554","url":null,"abstract":"We propose a surface-based cloth shading model that generates realistic cloth appearance with ply-level details. It generalizes previous surface-based models to a broader set of cloth including knitted and thin woven cloth. Our model takes into account the most dominant visual features of cloth, including anisotropic S-shaped reflection highlight, cross-shaped transmission highlights, delta transmission, and shadowing masking. We model these elements via a comprehensive micro-scale BSDF and a meso-scale effective BSDF formulation. Then, we propose an implementation that leverages the Monte Carlo sampler of path tracing for reducing precomputation to the bare minimum, by evaluating the effective BSDF as a Monte Carlo estimate, and encoding visibility using anisotropic spherical Gaussians. We demonstrate our model by replicating a set of woven and knitted fabrics, showing good match with respect to captured photographs.","PeriodicalId":280036,"journal":{"name":"ACM SIGGRAPH 2023 Conference Proceedings","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114095269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"MoiréTag: Angular Measurement and Tracking with a Passive Marker","authors":"Simeng Qiu, Hadi Amata, W. Heidrich","doi":"10.1145/3588432.3591538","DOIUrl":"https://doi.org/10.1145/3588432.3591538","url":null,"abstract":"Stable, low-cost, and precise visual measurement of directional information has many applications in domains such as virtual and augmented reality, visual odometry, or industrial computer vision. Conventional approaches like checkerboard patterns require careful pre-calibration, and can therefore not be operated in snapshot mode. Other optical methods like autocollimators offer very high precision but require controlled environments and are hard to take outside the lab. Non-optical methods like IMUs are low cost and widely available, but suffer from high drift errors. To overcome these challenges, we propose a novel snapshot method for angular measurement and tracking with Moiré patterns that are generated by binary structures printed on both sides of a glass plate. The Moiré effect amplifies minute angular shifts and translates them into spatial phase shifts that can be readily measured with a camera, effectively implementing an optical Vernier scale. We further extend this principle from a simple phase shift to a chirp model, which allows for full 6D tracking as well as estimation of camera intrinsics like the field of view. Simulation and experimental results show that the proposed non-contact object tracking framework is computationally efficient and the average angular accuracy of 0.17° outperforms the state-of-the-arts.","PeriodicalId":280036,"journal":{"name":"ACM SIGGRAPH 2023 Conference Proceedings","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123206324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adéla Šubrtová, Michal Lukáč, Jan Čech, David Futschik, Eli Shechtman, Daniel Sýkora
{"title":"Diffusion Image Analogies","authors":"Adéla Šubrtová, Michal Lukáč, Jan Čech, David Futschik, Eli Shechtman, Daniel Sýkora","doi":"10.1145/3588432.3591558","DOIUrl":"https://doi.org/10.1145/3588432.3591558","url":null,"abstract":"In this paper we present Diffusion Image Analogies—an example-based image editing approach that builds upon the concept of image analogies originally introduced by Hertzmann et al. [2001]. Given a pair of images that specify the intent of a specific transition, our approach enables to modify the target image in a way that it follows the analogy specified by this exemplar. In contrast to previous techniques which were able to capture analogies mostly on the low-level textural details our approach handles also changes in higher level semantics including transition of object domain, change of facial expression, or stylization. Although similar modifications can be achieved using diffusion models guided by text prompts [Rombach et al. 2022] our approach can operate solely in the domain of images without the need to specify the user’s intent using textual form. We demonstrate power of our approach in various challenging scenarios where the specified analogy would be difficult to transfer using previous techniques.","PeriodicalId":280036,"journal":{"name":"ACM SIGGRAPH 2023 Conference Proceedings","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123343710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eugene d'Eon, Benedikt Bitterli, A. Weidlich, Tizian Zeltner
{"title":"Microfacet Theory for Non-Uniform Heightfields","authors":"Eugene d'Eon, Benedikt Bitterli, A. Weidlich, Tizian Zeltner","doi":"10.1145/3588432.3591486","DOIUrl":"https://doi.org/10.1145/3588432.3591486","url":null,"abstract":"We propose new methods for combining NDFs in microfacet theory, enabling a wider range of surface statistics. The new BSDFs that follow allow for independent adjustment of appearance at grazing angles, and can’t be represented by linear blends of single-NDF BSDFs. We derive importance sampling for a symmetric operator that blends NDFs uniformly, and introduce a new asymmetric operator that supports NDF variation with elevation. We also extend Smith’s model to support piecewise-constant NDF and material variations with elevation, and demonstrate accuracy via Monte Carlo simulations.","PeriodicalId":280036,"journal":{"name":"ACM SIGGRAPH 2023 Conference Proceedings","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122586296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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