Objects with thin features and fine details are challenging for most multi-view stereo techniques since such features occupy small volumes and are usually only visible in a small portion of the available views. In this paper, we present an efficient algorithm to reconstruct intricate objects using densely sampled light fields. At the heart of our technique lies a novel approach to compute per-pixel depth values by exploiting local gradient information in densely sampled light fields. This approach can generate accurate depth values for very thin features and can be run for each pixel in parallel. We assess the reliability of our depth estimates using a novel two-sided photoconsistency measure, which can capture whether the pixel lies on a texture or a silhouette edge. This information is then used to propagate the depth estimates at high gradient regions to smooth parts of the views efficiently and reliably using edge-aware filtering. In the last step, the per-image depth values and color information are aggregated in 3D space using a voting scheme, allowing the reconstruction of a globally consistent mesh for the object. Our approach can process large video datasets very efficiently and at the same time generates high-quality object reconstructions that compare favorably to the results of state-of-the-art multi-view stereo methods.
The documents contained in these directories are included by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.