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Graph."],"published-print":{"date-parts":[[2017,12,31]]},"abstract":"<jats:p>We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and\/or simple scene models, we train an end-to-end deep neural network that directly regresses a limited field-of-view photo to HDR illumination, without strong assumptions on scene geometry, material properties, or lighting. We show that this can be accomplished in a three step process: 1) we train a robust lighting classifier to automatically annotate the location of light sources in a large dataset of LDR environment maps, 2) we use these annotations to train a deep neural network that predicts the location of lights in a scene from a single limited field-of-view photo, and 3) we fine-tune this network using a small dataset of HDR environment maps to predict light intensities. This allows us to automatically recover high-quality HDR illumination estimates that significantly outperform previous state-of-the-art methods. Consequently, using our illumination estimates for applications like 3D object insertion, produces photo-realistic results that we validate via a perceptual user study.<\/jats:p>","DOI":"10.1145\/3130800.3130891","type":"journal-article","created":{"date-parts":[[2017,11,22]],"date-time":"2017-11-22T11:25:08Z","timestamp":1511349908000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":231,"title":["Learning to predict indoor illumination from a single image"],"prefix":"10.1145","volume":"36","author":[{"given":"Marc-Andr\u00e9","family":"Gardner","sequence":"first","affiliation":[{"name":"Universit\u00e9 Laval"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalyan","family":"Sunkavalli","sequence":"additional","affiliation":[{"name":"Adobe Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ersin","family":"Yumer","sequence":"additional","affiliation":[{"name":"Adobe Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Shen","sequence":"additional","affiliation":[{"name":"Adobe Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emiliano","family":"Gambaretto","sequence":"additional","affiliation":[{"name":"Adobe Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Gagn\u00e9","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Laval"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Fran\u00e7ois","family":"Lalonde","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Laval"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,11,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.642"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12249"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.10"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2377712"},{"key":"e_1_2_1_5_1","volume-title":"Material Recognition in the Wild with the Materials in Context Database. 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