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As the depth data of these sensors is noisy, truncated signed distance fields are typically used to regularize out the noise, which unfortunately leads to over-smoothed results. In our approach, we leverage RGB data to refine these reconstructions through shading cues, as color input is typically of much higher resolution than the depth data. As a result, we obtain reconstructions with high geometric detail, far beyond the depth resolution of the camera itself. Our core contribution is shading-based refinement directly on the implicit surface representation, which is generated from globally-aligned RGB-D images. We formulate the inverse shading problem on the volumetric distance field, and present a novel objective function which jointly optimizes for fine-scale surface geometry and spatially-varying surface reflectance. In order to enable the efficient reconstruction of sub-millimeter detail, we store and process our surface using a sparse voxel hashing scheme which we augment by introducing a grid hierarchy. A tailored GPU-based Gauss-Newton solver enables us to refine large shape models to previously unseen resolution within only a few seconds.<\/jats:p>","DOI":"10.1145\/2766887","type":"journal-article","created":{"date-parts":[[2015,7,28]],"date-time":"2015-07-28T12:26:38Z","timestamp":1438086398000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":109,"title":["Shading-based refinement on volumetric signed distance functions"],"prefix":"10.1145","volume":"34","author":[{"given":"Michael","family":"Zollh\u00f6fer","sequence":"first","affiliation":[{"name":"University of Erlangen-Nuremberg and MPI Informatics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angela","family":"Dai","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias","family":"Innmann","sequence":"additional","affiliation":[{"name":"University of Erlangen-Nuremberg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglei","family":"Wu","sequence":"additional","affiliation":[{"name":"ETH Zurich"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Stamminger","sequence":"additional","affiliation":[{"name":"University of Erlangen-Nuremberg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Theobalt","sequence":"additional","affiliation":[{"name":"MPI Informatics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias","family":"Nie\u00dfner","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2015,7,27]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2001269.2001293"},{"key":"e_1_2_2_2_1","unstructured":"AgiSoft L. 2014. 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