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Graph."],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>We present deferred neural lighting, a novel method for free-viewpoint relighting from unstructured photographs of a scene captured with handheld devices. Our method leverages a scene-dependent neural rendering network for relighting a rough geometric proxy with learnable neural textures. Key to making the rendering network lighting aware are radiance cues: global illumination renderings of a rough proxy geometry of the scene for a small set of basis materials and lit by the target lighting. As such, the light transport through the scene is never explicitely modeled, but resolved at rendering time by a neural rendering network. We demonstrate that the neural textures and neural renderer can be trained end-to-end from unstructured photographs captured with a double hand-held camera setup that concurrently captures the scene while being lit by only one of the cameras' flash lights. 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Graph."],"published-print":{"date-parts":[[2016,11,11]]},"abstract":"<jats:p>\n            We address the problem of autonomously exploring unknown objects in a scene by consecutive depth acquisitions. The goal is to reconstruct the scene while online identifying the objects from among a large collection of 3D shapes. Fine-grained shape identification demands a meticulous series of observations attending to varying views and parts of the object of interest. Inspired by the recent success of attention-based models for 2D recognition, we develop a\n            <jats:italic>3D Attention Model<\/jats:italic>\n            that selects the best views to scan from, as well as the most informative regions in each view to focus on, to achieve efficient object recognition. The region-level attention leads to focus-driven features which are quite robust against object occlusion. The attention model, trained with the 3D shape collection, encodes the temporal dependencies among consecutive views with deep recurrent networks. This facilitates order-aware view planning accounting for robot movement cost. In achieving instance identification, the shape collection is organized into a hierarchy, associated with pre-trained hierarchical classifiers. The effectiveness of our method is demonstrated on an autonomous robot (PR) that explores a scene and identifies the objects to construct a 3D scene model.\n          <\/jats:p>","DOI":"10.1145\/2980179.2980224","type":"journal-article","created":{"date-parts":[[2016,11,11]],"date-time":"2016-11-11T17:02:54Z","timestamp":1478883774000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["3D attention-driven depth acquisition for object identification"],"prefix":"10.1145","volume":"35","author":[{"given":"Kai","family":"Xu","sequence":"first","affiliation":[{"name":"National University of Defense Technology and Shandong University and Shenzhen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Shi","sequence":"additional","affiliation":[{"name":"National University of Defense Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lintao","family":"Zheng","sequence":"additional","affiliation":[{"name":"National University of Defense Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"SIAT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Liu","sequence":"additional","affiliation":[{"name":"National University of Defense Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University and SIAT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Su","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[{"name":"Tel-Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoquan","family":"Chen","sequence":"additional","affiliation":[{"name":"Shandong University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2016,12,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2014.2320795"},{"key":"e_1_2_1_2_1","unstructured":"Ba J. 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Graph."],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization, simulation evaluation and simulation guidance are also proposed. Together, our framework establishes a highly automated pipeline from raw data to crowd analysis, comparison and simulation guidance. 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Graph."],"published-print":{"date-parts":[[2005,7]]},"abstract":"<jats:p>\n            As there is no hardware support neither for rendering trimmed NURBS -- the standard surface representation in CAD -- nor for T-Spline surfaces the usability of existing rendering APIs like OpenGL, where a run-time tessellation is performed on the CPU, is limited to simple scenes. Due to the irregular mesh data structures required for trimming no algorithms exists that exploit the GPU for tessellation. Therefore, recent approaches perform a pretessellation and use level-of-detail techniques. In contrast to a simple API these methods require tedious preparation of the models before rendering and hinder interactive editing. 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