Patch Optimization for Surface Light Field Reconstruction
نویسندگان
چکیده
منابع مشابه
Light Field Reconstruction Using a Planar Patch Model
Light fields are known for their potential in generating 3D reconstructions of a scene from novel viewpoints without need for a model of the scene. Reconstruction of novel views, however, often leads to ghosting artefacts, which can be relieved by correcting for the depth of objects within the scene using disparity compensation. Unfortunately, reconstructions from this disparity information suf...
متن کاملEstimating Planar Patches for Light Field Reconstruction
Light fields are known for their potential in generating reconstructions of a scene from novel viewpoints without need for a model of the scene. Reconstruction of novel views, however, often leads to ghosting artefacts, which can be relieved by correcting for the depth of objects within the scene using disparity compensation. Unfortunately, reconstructions from the disparity information suffer ...
متن کاملSurface Patch Reconstruction From "One-Dimensional" Tactile Data
This paper studies the reconstruction of unknown curved surfaces through finger tracking. A patch can be generated from tactile data points along three concurrent surface curves under the Darboux frame estimated at the curve intersection point. Surface fitting while minimizing the total (absolute) Gaussian curvature effectively prevents unnecessary folds otherwise expected to result from the us...
متن کاملScene Reconstruction from a Light Field
This thesis addresses novel methods to reconstruct the scene from a light field. Light fields are sampled radiances at all points along all directions in the scene. We propose to reconstruct the scene by analyzing the structures of rays in the light field without any geometric assumption about the scene. We first explore the general characteristics of light fields, and important operations such...
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ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2018
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.2018edl8072