Production imaging pipelines commonly operate using fixed-point arithmetic, and within these pipelines a core primitive is convolution by small filters - taking; convex combinations of fixed-point values in order to resample, interpolate, or denoise. We describe a new way to compute unbiased convex combinations of fixed-point values using sequences of averaging instructions, which exist on all popular CPU and DSP architectures but are seldom used. For a variety of popular kernels, our averaging; trees have higher performance and higher quality than existing standard practice.
{"title":"Better Fixed-Point Filtering with Averaging Trees","authors":"Andrew Adams, Dillon Sharlet","doi":"10.1145/3543869","DOIUrl":"https://doi.org/10.1145/3543869","url":null,"abstract":"Production imaging pipelines commonly operate using fixed-point arithmetic, and within these pipelines a core primitive is convolution by small filters - taking; convex combinations of fixed-point values in order to resample, interpolate, or denoise. We describe a new way to compute unbiased convex combinations of fixed-point values using sequences of averaging instructions, which exist on all popular CPU and DSP architectures but are seldom used. For a variety of popular kernels, our averaging; trees have higher performance and higher quality than existing standard practice.","PeriodicalId":74536,"journal":{"name":"Proceedings of the ACM on computer graphics and interactive techniques","volume":"5 1","pages":"1 - 8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41624872","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}
Adding motion blur to a scene can help to convey the feeling of speed even at low frame rates. Monte Carlo ray tracing can compute accurate motion blur, but requires a large number of samples per pixel to converge. In comparison, rasterization, in combination with a post-processing filter, can generate fast, but not accurate motion blur from a single sample per pixel. We build upon a recent path tracing denoiser and propose its variant to simulate ray-traced motion blur, enabling fast and high-quality motion blur from a single sample per pixel. Our approach creates temporally coherent renderings by estimating the motion direction and variance locally, and using these estimates to guide wavelet filters at different scales. We compare image quality against brute force Monte Carlo methods and current post-processing motion blur. Our approach achieves real-time frame rates, requiring less than 4ms for full-screen motion blur at a resolution of 1920 x 1080 on recent graphics cards.
在场景中添加动态模糊可以帮助在低帧率下传达速度感。蒙特卡罗光线跟踪可以计算精确的运动模糊，但需要大量的样本每像素收敛。相比之下，栅格化与后处理滤波器相结合，可以从每个像素的单个样本中生成快速但不准确的运动模糊。我们建立在最近的路径跟踪去噪，并提出其变体来模拟光线跟踪运动模糊，实现快速和高质量的运动模糊从单个样本每像素。我们的方法通过估计局部运动方向和方差来创建时间连贯的渲染，并使用这些估计来指导不同尺度的小波滤波器。我们将图像质量与蛮力蒙特卡罗方法和当前的后处理运动模糊进行比较。我们的方法实现了实时帧率，在最近的显卡上，在1920 x 1080分辨率下，全屏运动模糊需要不到4毫秒。
{"title":"Spatiotemporal Variance-Guided Filtering for Motion Blur","authors":"Max Oberberger, M. Chajdas, R. Westermann","doi":"10.1145/3543871","DOIUrl":"https://doi.org/10.1145/3543871","url":null,"abstract":"Adding motion blur to a scene can help to convey the feeling of speed even at low frame rates. Monte Carlo ray tracing can compute accurate motion blur, but requires a large number of samples per pixel to converge. In comparison, rasterization, in combination with a post-processing filter, can generate fast, but not accurate motion blur from a single sample per pixel. We build upon a recent path tracing denoiser and propose its variant to simulate ray-traced motion blur, enabling fast and high-quality motion blur from a single sample per pixel. Our approach creates temporally coherent renderings by estimating the motion direction and variance locally, and using these estimates to guide wavelet filters at different scales. We compare image quality against brute force Monte Carlo methods and current post-processing motion blur. Our approach achieves real-time frame rates, requiring less than 4ms for full-screen motion blur at a resolution of 1920 x 1080 on recent graphics cards.","PeriodicalId":74536,"journal":{"name":"Proceedings of the ACM on computer graphics and interactive techniques","volume":"5 1","pages":"1 - 13"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45193724","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}
We introduce virtual blue noise lighting, a rendering pipeline for estimating indirect illumination with a blue noise distribution of virtual lights. Our pipeline is designed for virtual lights with non-uniform emission profiles that are more expensive to store, but required for properly and efficiently handling specular transport. Unlike the typical virtual light placement approaches that traverse light paths from the original light sources, we generate them starting from the camera. This avoids two important problems: wasted memory and computation with fully-occluded virtual lights, and excessive virtual light density around high-probability light paths. In addition, we introduce a parallel and adaptive sample elimination strategy to achieve a blue noise distribution of virtual lights with varying density. This addresses the third problem of virtual light placement by ensuring that they are not placed too close to each other, providing better coverage of the (indirectly) visible surfaces and further improving the quality of the final lighting estimation. For computing the virtual light emission profiles, we present a photon splitting technique that allows efficiently using a large number of photons, as it does not require storing them. During lighting estimation, our method allows using both global power-based and local BSDF important sampling techniques, combined via multiple importance sampling. In addition, we present an adaptive path extension method that avoids sampling nearby virtual lights for reducing the estimation error. We show that our method significantly outperforms path tracing and prior work in virtual lights in terms of both performance and image quality, producing a fast but biased estimate of global illumination.
{"title":"Virtual Blue Noise Lighting","authors":"Tianyu Li, Wenyou Wang, Daqi Lin, Cem Yuksel","doi":"10.1145/3543872","DOIUrl":"https://doi.org/10.1145/3543872","url":null,"abstract":"We introduce virtual blue noise lighting, a rendering pipeline for estimating indirect illumination with a blue noise distribution of virtual lights. Our pipeline is designed for virtual lights with non-uniform emission profiles that are more expensive to store, but required for properly and efficiently handling specular transport. Unlike the typical virtual light placement approaches that traverse light paths from the original light sources, we generate them starting from the camera. This avoids two important problems: wasted memory and computation with fully-occluded virtual lights, and excessive virtual light density around high-probability light paths. In addition, we introduce a parallel and adaptive sample elimination strategy to achieve a blue noise distribution of virtual lights with varying density. This addresses the third problem of virtual light placement by ensuring that they are not placed too close to each other, providing better coverage of the (indirectly) visible surfaces and further improving the quality of the final lighting estimation. For computing the virtual light emission profiles, we present a photon splitting technique that allows efficiently using a large number of photons, as it does not require storing them. During lighting estimation, our method allows using both global power-based and local BSDF important sampling techniques, combined via multiple importance sampling. In addition, we present an adaptive path extension method that avoids sampling nearby virtual lights for reducing the estimation error. We show that our method significantly outperforms path tracing and prior work in virtual lights in terms of both performance and image quality, producing a fast but biased estimate of global illumination.","PeriodicalId":74536,"journal":{"name":"Proceedings of the ACM on computer graphics and interactive techniques","volume":"5 1","pages":"1 - 26"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47642931","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}
We introduce per-halfedge texturing (Htex) a GPU-friendly method for texturing arbitrary polygon-meshes without an explicit parameterization. Htex builds upon the insight that halfedges encode an intrinsic triangulation for polygon meshes, where each halfedge spans a unique triangle with direct adjacency information. Rather than storing a separate texture per face of the input mesh as is done by previous parameterization-free texturing methods, Htex stores a square texture for each halfedge and its twin. We show that this simple change from face to halfedge induces two important properties for high performance parameterization-free texturing. First, Htex natively supports arbitrary polygons without requiring dedicated code for, e.g, non-quad faces. Second, Htex leads to a straightforward and efficient GPU implementation that uses only three texture-fetches per halfedge to produce continuous texturing across the entire mesh. We demonstrate the effectiveness of Htex by rendering production assets in real time.
{"title":"Htex","authors":"Wilhem Barbier, J. Dupuy","doi":"10.1145/3543868","DOIUrl":"https://doi.org/10.1145/3543868","url":null,"abstract":"We introduce per-halfedge texturing (Htex) a GPU-friendly method for texturing arbitrary polygon-meshes without an explicit parameterization. Htex builds upon the insight that halfedges encode an intrinsic triangulation for polygon meshes, where each halfedge spans a unique triangle with direct adjacency information. Rather than storing a separate texture per face of the input mesh as is done by previous parameterization-free texturing methods, Htex stores a square texture for each halfedge and its twin. We show that this simple change from face to halfedge induces two important properties for high performance parameterization-free texturing. First, Htex natively supports arbitrary polygons without requiring dedicated code for, e.g, non-quad faces. Second, Htex leads to a straightforward and efficient GPU implementation that uses only three texture-fetches per halfedge to produce continuous texturing across the entire mesh. We demonstrate the effectiveness of Htex by rendering production assets in real time.","PeriodicalId":74536,"journal":{"name":"Proceedings of the ACM on computer graphics and interactive techniques","volume":"5 1","pages":"1 - 14"},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46429627","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}
Laurent Belcour, T. Deliot, Wilhem Barbier, C. Soler
In this work, we explore a change of paradigm to build Precomputed Radiance Transfer (PRT) methods in a data-driven way. This paradigm shift allows us to alleviate the difficulties of building traditional PRT methods such as defining a reconstruction basis, coding a dedicated path tracer to compute a transfer function, etc. Our objective is to pave the way for Machine Learned methods by providing a simple baseline algorithm. More specifically, we demonstrate real-time rendering of indirect illumination in hair and surfaces from a few measurements of direct lighting. We build our baseline from pairs of direct and indirect illumination renderings using only standard tools such as Singular Value Decomposition (SVD) to extract both the reconstruction basis and transfer function.
{"title":"A Data-Driven Paradigm for Precomputed Radiance Transfer","authors":"Laurent Belcour, T. Deliot, Wilhem Barbier, C. Soler","doi":"10.1145/3543864","DOIUrl":"https://doi.org/10.1145/3543864","url":null,"abstract":"In this work, we explore a change of paradigm to build Precomputed Radiance Transfer (PRT) methods in a data-driven way. This paradigm shift allows us to alleviate the difficulties of building traditional PRT methods such as defining a reconstruction basis, coding a dedicated path tracer to compute a transfer function, etc. Our objective is to pave the way for Machine Learned methods by providing a simple baseline algorithm. More specifically, we demonstrate real-time rendering of indirect illumination in hair and surfaces from a few measurements of direct lighting. We build our baseline from pairs of direct and indirect illumination renderings using only standard tools such as Singular Value Decomposition (SVD) to extract both the reconstruction basis and transfer function.","PeriodicalId":74536,"journal":{"name":"Proceedings of the ACM on computer graphics and interactive techniques","volume":" ","pages":"1 - 15"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45648275","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}
Maike P. Stoeve, M. Wirth, Rosanna Farlock, André Antunovic, Victoria Müller, Bjoern M. Eskofier
Monitoring stress is relevant in many areas, including sports science. In that scope, various studies showed the feasibility of stress classification using eye tracking data. In most cases, the screen-based experimental design restricted the motion of participants. Consequently, the transferability of results to dynamic sports applications remains unclear. To address this research gap, we conducted a virtual reality-based stress test consisting of a football goalkeeping scenario. We contribute by proposing a stress classification pipeline solely relying on gaze behaviour and pupil diameter metrics extracted from the recorded data. To optimize the analysis pipeline, we applied feature selection and compared the performance of different classification methods. Results show that the Random Forest classifier achieves the best performance with 87.3% accuracy, comparable to state-of-the-art approaches fusing eye tracking data and additional biosignals. Moreover, our approach outperforms existing methods exclusively relying on eye measures.
{"title":"Eye Tracking-Based Stress Classification of Athletes in Virtual Reality","authors":"Maike P. Stoeve, M. Wirth, Rosanna Farlock, André Antunovic, Victoria Müller, Bjoern M. Eskofier","doi":"10.1145/3530796","DOIUrl":"https://doi.org/10.1145/3530796","url":null,"abstract":"Monitoring stress is relevant in many areas, including sports science. In that scope, various studies showed the feasibility of stress classification using eye tracking data. In most cases, the screen-based experimental design restricted the motion of participants. Consequently, the transferability of results to dynamic sports applications remains unclear. To address this research gap, we conducted a virtual reality-based stress test consisting of a football goalkeeping scenario. We contribute by proposing a stress classification pipeline solely relying on gaze behaviour and pupil diameter metrics extracted from the recorded data. To optimize the analysis pipeline, we applied feature selection and compared the performance of different classification methods. Results show that the Random Forest classifier achieves the best performance with 87.3% accuracy, comparable to state-of-the-art approaches fusing eye tracking data and additional biosignals. Moreover, our approach outperforms existing methods exclusively relying on eye measures.","PeriodicalId":74536,"journal":{"name":"Proceedings of the ACM on computer graphics and interactive techniques","volume":" ","pages":"1 - 17"},"PeriodicalIF":0.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48978784","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}
Marie-Lena Eckert, T. Robotham, Emanuël Habets, Olli S. Rummukainen
Virtual reality (VR) headsets with an integrated eye tracker enable the measurement of pupil size fluctuations correlated with cognition during a VR experience. We present a method to correct for the light-induced pupil size changes, otherwise masking the more subtle cognitively-driven effects, such as cognitive load and emotional state. We explore multiple calibration sequences to find individual mapping functions relating the luminance to pupil dilation that can be employed in real-time during a VR experience. The resulting mapping functions are evaluated in a VR-based n-back task and in free exploration of a six-degrees-of-freedom VR scene. Our results show estimating luminance from a weighted average of the fixation area and the background yields the best performance. Calibration sequence composed of either solid gray or realistic scene brightness levels shown for 6 s in a pseudo-random order proved most robust.
{"title":"Pupillary Light Reflex Correction for Robust Pupillometry in Virtual Reality","authors":"Marie-Lena Eckert, T. Robotham, Emanuël Habets, Olli S. Rummukainen","doi":"10.1145/3530798","DOIUrl":"https://doi.org/10.1145/3530798","url":null,"abstract":"Virtual reality (VR) headsets with an integrated eye tracker enable the measurement of pupil size fluctuations correlated with cognition during a VR experience. We present a method to correct for the light-induced pupil size changes, otherwise masking the more subtle cognitively-driven effects, such as cognitive load and emotional state. We explore multiple calibration sequences to find individual mapping functions relating the luminance to pupil dilation that can be employed in real-time during a VR experience. The resulting mapping functions are evaluated in a VR-based n-back task and in free exploration of a six-degrees-of-freedom VR scene. Our results show estimating luminance from a weighted average of the fixation area and the background yields the best performance. Calibration sequence composed of either solid gray or realistic scene brightness levels shown for 6 s in a pseudo-random order proved most robust.","PeriodicalId":74536,"journal":{"name":"Proceedings of the ACM on computer graphics and interactive techniques","volume":" ","pages":"1 - 16"},"PeriodicalIF":0.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41831796","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}
Matheus Prado Prandini Faria, E. Julia, M. Z. Nascimento, Rita Maria Silva Julia
Video games, in addition to representing an extremely relevant field of entertainment and market, have been widely used as a case study in artificial intelligence for representing a problem with a high degree of complexity. In such studies, the investigation of approaches that endow player agents with the ability to retrieve relevant information from game scenes stands out, since such information can be very useful to improve their learning ability. This work proposes and analyses new deep learning-based models to identify game events occurring in Super Mario Bros gameplay footage. The architecture of each model is composed of a feature extractor convolutional neural network (CNN) and a classifier neural network (NN). The extracting CNN aims to produce a feature-based representation for game scenes and submit it to the classifier, so that the latter can identify the game event present in each scene. The models differ from each other according to the following elements: the type of the CNN; the type of the NN classifier; and the type of the game scene representation at the CNN input, being either single frames, or chunks, which are n-sequential frames (in this paper 6 frames were used per chunk) grouped into a single input. The main contribution of this article is to demonstrate the greater performance reached by the models which combines the chunk representation for the game scenes with the resources of the classifier recurrent neural networks (RNN).
{"title":"Investigating the Performance of Various Deep Neural Networks-based Approaches Designed to Identify Game Events in Gameplay Footage","authors":"Matheus Prado Prandini Faria, E. Julia, M. Z. Nascimento, Rita Maria Silva Julia","doi":"10.1145/3522624","DOIUrl":"https://doi.org/10.1145/3522624","url":null,"abstract":"Video games, in addition to representing an extremely relevant field of entertainment and market, have been widely used as a case study in artificial intelligence for representing a problem with a high degree of complexity. In such studies, the investigation of approaches that endow player agents with the ability to retrieve relevant information from game scenes stands out, since such information can be very useful to improve their learning ability. This work proposes and analyses new deep learning-based models to identify game events occurring in Super Mario Bros gameplay footage. The architecture of each model is composed of a feature extractor convolutional neural network (CNN) and a classifier neural network (NN). The extracting CNN aims to produce a feature-based representation for game scenes and submit it to the classifier, so that the latter can identify the game event present in each scene. The models differ from each other according to the following elements: the type of the CNN; the type of the NN classifier; and the type of the game scene representation at the CNN input, being either single frames, or chunks, which are n-sequential frames (in this paper 6 frames were used per chunk) grouped into a single input. The main contribution of this article is to demonstrate the greater performance reached by the models which combines the chunk representation for the game scenes with the resources of the classifier recurrent neural networks (RNN).","PeriodicalId":74536,"journal":{"name":"Proceedings of the ACM on computer graphics and interactive techniques","volume":" ","pages":"1 - 17"},"PeriodicalIF":0.0,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42278679","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}
Joao Liborio Cardoso, B. Kerbl, Lei Yang, Yury Uralsky, M. Wimmer
Visual error metrics play a fundamental role in the quantification of perceived image similarity. Most recently, use cases for them in real-time applications have emerged, such as content-adaptive shading and shading reuse to increase performance and improve efficiency. A wide range of different metrics has been established, with the most sophisticated being capable of capturing the perceptual characteristics of the human visual system. However, their complexity, computational expense, and reliance on reference images to compare against prevent their generalized use in real-time, restricting such applications to using only the simplest available metrics. In this work, we explore the abilities of convolutional neural networks to predict a variety of visual metrics without requiring either reference or rendered images. Specifically, we train and deploy a neural network to estimate the visual error resulting from reusing shading or using reduced shading rates. The resulting models account for 70%-90% of the variance while achieving up to an order of magnitude faster computation times. Our solution combines image-space information that is readily available in most state-of-the-art deferred shading pipelines with reprojection from previous frames to enable an adequate estimate of visual errors, even in previously unseen regions. We describe a suitable convolutional network architecture and considerations for data preparation for training. We demonstrate the capability of our network to predict complex error metrics at interactive rates in a real-time application that implements content-adaptive shading in a deferred pipeline. Depending on the portion of unseen image regions, our approach can achieve up to 2x performance compared to state-of-the-art methods.
{"title":"Training and Predicting Visual Error for Real-Time Applications","authors":"Joao Liborio Cardoso, B. Kerbl, Lei Yang, Yury Uralsky, M. Wimmer","doi":"10.1145/3522625","DOIUrl":"https://doi.org/10.1145/3522625","url":null,"abstract":"Visual error metrics play a fundamental role in the quantification of perceived image similarity. Most recently, use cases for them in real-time applications have emerged, such as content-adaptive shading and shading reuse to increase performance and improve efficiency. A wide range of different metrics has been established, with the most sophisticated being capable of capturing the perceptual characteristics of the human visual system. However, their complexity, computational expense, and reliance on reference images to compare against prevent their generalized use in real-time, restricting such applications to using only the simplest available metrics. In this work, we explore the abilities of convolutional neural networks to predict a variety of visual metrics without requiring either reference or rendered images. Specifically, we train and deploy a neural network to estimate the visual error resulting from reusing shading or using reduced shading rates. The resulting models account for 70%-90% of the variance while achieving up to an order of magnitude faster computation times. Our solution combines image-space information that is readily available in most state-of-the-art deferred shading pipelines with reprojection from previous frames to enable an adequate estimate of visual errors, even in previously unseen regions. We describe a suitable convolutional network architecture and considerations for data preparation for training. We demonstrate the capability of our network to predict complex error metrics at interactive rates in a real-time application that implements content-adaptive shading in a deferred pipeline. Depending on the portion of unseen image regions, our approach can achieve up to 2x performance compared to state-of-the-art methods.","PeriodicalId":74536,"journal":{"name":"Proceedings of the ACM on computer graphics and interactive techniques","volume":" ","pages":"1 - 17"},"PeriodicalIF":0.0,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45291822","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}
Zhiying Fu, Rui Xu, Shiqing Xin, Shuangmin Chen, Changhe Tu, Chenglei Yang, Lin Lu
The latest innovations of VR make it possible to construct 3D models in a holographic immersive simulation environment. In this paper, we develop a user-friendly mid-air interactive modeling system named EasyVRModeling. We first prepare a dataset consisting of diverse components and precompute the discrete signed distance function (SDF) for each component. During the modeling phase, users can freely design complicated shapes with a pair of VR controllers. Based on the discrete SDF representation, any CSG-like operation (union, intersect, subtract) can be performed voxel-wise. Throughout the modeling process, we maintain one single dynamic SDF for the whole scene so that the zero-level set surface of the SDF exactly encodes the up-to-date constructed shape. Both SDF fusion and surface extraction are implemented via GPU to allow for smooth user experience. We asked 34 volunteers to create their favorite models using EasyVRModeling. With a simple training process for several minutes, most of them can create a fascinating shape or even a descriptive scene very quickly.
{"title":"EasyVRModeling: Easily Create 3D Models by an Immersive VR System","authors":"Zhiying Fu, Rui Xu, Shiqing Xin, Shuangmin Chen, Changhe Tu, Chenglei Yang, Lin Lu","doi":"10.1145/3522613","DOIUrl":"https://doi.org/10.1145/3522613","url":null,"abstract":"The latest innovations of VR make it possible to construct 3D models in a holographic immersive simulation environment. In this paper, we develop a user-friendly mid-air interactive modeling system named EasyVRModeling. We first prepare a dataset consisting of diverse components and precompute the discrete signed distance function (SDF) for each component. During the modeling phase, users can freely design complicated shapes with a pair of VR controllers. Based on the discrete SDF representation, any CSG-like operation (union, intersect, subtract) can be performed voxel-wise. Throughout the modeling process, we maintain one single dynamic SDF for the whole scene so that the zero-level set surface of the SDF exactly encodes the up-to-date constructed shape. Both SDF fusion and surface extraction are implemented via GPU to allow for smooth user experience. We asked 34 volunteers to create their favorite models using EasyVRModeling. With a simple training process for several minutes, most of them can create a fascinating shape or even a descriptive scene very quickly.","PeriodicalId":74536,"journal":{"name":"Proceedings of the ACM on computer graphics and interactive techniques","volume":"5 1","pages":"1 - 14"},"PeriodicalIF":0.0,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64049769","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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