SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision最新文献

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Blind recovery of spatially varying reflectance from a single image 单幅图像空间变化反射率的盲恢复
SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision Pub Date : 2014-11-24 DOI: 10.1145/2670291.2670293
Kevin Karsch, D. Forsyth
{"title":"Blind recovery of spatially varying reflectance from a single image","authors":"Kevin Karsch, D. Forsyth","doi":"10.1145/2670291.2670293","DOIUrl":"https://doi.org/10.1145/2670291.2670293","url":null,"abstract":"We propose a new technique for estimating spatially varying parametric materials from a single image of an object with unknown shape in unknown illumination. Our method uses a low-order parametric reflectance model, and incorporates strong assumptions about lighting and shape. We develop new priors about how materials mix over space, and jointly infer all of these properties from a single image. This produces a decomposition of an image which corresponds, in one sense, to microscopic features (material reflectance) and macroscopic features (weights defining the mixing properties of materials over space). We have built a large dataset of real objects rendered with different material models under different illumination fields for training and ground truth evaluation. Extensive experiments on both our synthetic dataset images as well as real images show that (a) our method recovers parameters with reasonable accuracy; (b) material parameters recovered by our method give accurate predictions of new renderings of the object; and (c) our low-order reflectance model still provides a good fit to many real-world reflectances.","PeriodicalId":148310,"journal":{"name":"SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131722724","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}
引用次数: 2
MCGraph: multi-criterion representation for scene understanding MCGraph:场景理解的多准则表示
SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision Pub Date : 2014-11-24 DOI: 10.1145/2670291.2670292
M. Hueting, Áron Monszpart, Nicolas Mellado
{"title":"MCGraph: multi-criterion representation for scene understanding","authors":"M. Hueting, Áron Monszpart, Nicolas Mellado","doi":"10.1145/2670291.2670292","DOIUrl":"https://doi.org/10.1145/2670291.2670292","url":null,"abstract":"The field of scene understanding endeavours to extract a broad range of information from 3D scenes. Current approaches exploit one or at most a few different criteria (e.g., spatial, semantic, functional information) simultaneously for analysis. We argue that to take scene understanding to the next level of performance, we need to take into account many different, and possibly previously unconsidered types of knowledge simultaneously. A unified representation for this type of processing is as of yet missing. In this work we propose MCGraph: a unified multi-criterion data representation for understanding and processing of large-scale 3D scenes. Scene abstraction and prior knowledge are kept separated, but highly connected. For this purpose, primitives (i.e., proxies) and their relationships (e.g., contact, support, hierarchical) are stored in an abstraction graph, while the different categories of prior knowledge necessary for processing are stored separately in a knowledge graph. These graphs complement each other bidirectionally, and are processed concurrently. We illustrate our approach by expressing previous techniques using our formulation, and present promising avenues of research opened up by using such a representation. We also distribute a set of MCGraph annotations for a small number of NYU2 scenes, to be used as ground truth multi-criterion abstractions.","PeriodicalId":148310,"journal":{"name":"SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision","volume":"35 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113939259","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}
引用次数: 7
On being the right scale: sizing large collections of 3D models 关于正确的比例:调整大型3D模型集合的大小
SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision Pub Date : 2014-11-24 DOI: 10.1145/2670291.2670295
M. Savva, Angel X. Chang, G. Bernstein, Christopher D. Manning, P. Hanrahan
{"title":"On being the right scale: sizing large collections of 3D models","authors":"M. Savva, Angel X. Chang, G. Bernstein, Christopher D. Manning, P. Hanrahan","doi":"10.1145/2670291.2670295","DOIUrl":"https://doi.org/10.1145/2670291.2670295","url":null,"abstract":"We address the problem of recovering reliable sizes for a collection of models defined using scales with unknown correspondence to physical units. Our algorithmic approach provides absolute size estimates for 3D models by combining category-based size priors and size observations from 3D scenes. Our approach handles un-observed 3D models without any user intervention. It also scales to large public 3D model databases and is appropriate for handling the open-world problem of rapidly expanding collections of 3D models. We use two datasets from online 3D model repositories to evaluate against both human judgments of size and ground truth physical sizes of 3D models, and find that an algorithmic approach can predict sizes more accurately than people.","PeriodicalId":148310,"journal":{"name":"SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116214548","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}
引用次数: 22
Annotating RGBD images of indoor scenes 对室内场景的RGBD图像进行标注
SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision Pub Date : 2014-11-24 DOI: 10.1145/2670291.2670294
Yu-Shiang Wong, Hung-Kuo Chu
{"title":"Annotating RGBD images of indoor scenes","authors":"Yu-Shiang Wong, Hung-Kuo Chu","doi":"10.1145/2670291.2670294","DOIUrl":"https://doi.org/10.1145/2670291.2670294","url":null,"abstract":"Annotating RGBD images with high quality semantic annotations plays a crucial key to the advanced scene understanding and image manipulation. While the popularity of affordable RGBD sensors has eased the process to acquire RGBD images, annotating them, automatically or manually, is still a challenging task. State-of-the-art annotation tools focus only on 2D operations and provide at most image segmentation and object labels even with the presence of depth data. In this work, we present an interactive system to exploit both color and depth cues and facilitate annotating RGBD images with image and scene level segmentation, object labels and 3D geometry and structures. With our system, the users only have to provide few scribbles to identify object instances and specify the label and support relationships of objects, while the system performs those tedious tasks of segmenting image and estimating the 3D cuboids. We test the system on a subset of benchmark RGBD dataset and demonstrate that our system provides a convenient way to generate a baseline dataset with rich semantic annotations.","PeriodicalId":148310,"journal":{"name":"SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125268475","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}
引用次数: 1
SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision 2014年亚洲室内场景:图形与视觉的结合
{"title":"SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision","authors":"","doi":"10.1145/2670291","DOIUrl":"https://doi.org/10.1145/2670291","url":null,"abstract":"","PeriodicalId":148310,"journal":{"name":"SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision","volume":"429 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116279120","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}
引用次数: 1
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