Fusing Structured Light Consistency and Helmholtz Normals for 3D Reconstruction

Abstract

In this paper, we propose a 3D reconstruction approach which combines a structured light based consistency measure with dense normal information obtained by exploiting the Helmholtz reciprocity principle. This combination compensates for the individual limitations of techniques providing normal information, which are mainly affected by low-frequency drift, and those providing positional information, which are often not well-suited to recover fine details. To obtain Helmholtz reciprocal samples, we employ a turntable-based setup. Due to the reciprocity, the structured light directly provides the occlusion information needed during the normal estimation for both the cameras and light sources. We perform the reconstruction by solving one global variational problem which integrates all available measurements simultaneously, over all cameras, light source positions and turntable rotations. For this, we employ an octree-based continuous min-cut framework in order to alleviate metrification errors while maintaining memory efficiency. We evaluate the performance of our algorithm both on synthetic and real-world data.

3D Data Acquisition and Registration Using Two Opposing Kinects

“…There are many existing works on the registration of multiview range images obtained by photometric stereo and structured light techniques, such as [4,17,37,39,46], with some summative works e.g. in [18,42,43].…”

WebGL-based streaming and presentation of objects with bidirectional texture functions

“…For other objects, such as the Ganesh figurine in Figure 1 as well as the wax moulage in Figure 11, we acquired the data with a more recent setup developed at our institute [Weinmann et al 2012;Schwartz and Klein 2012] (see Figure 2b). This demonstrates that our streaming and rendering proposed in this paper works independent from the applied acquisition technique and thus other setups tailored for the image-based acquisition of geometry and reflectance, such as [Holroyd et al 2010] and [Köhler et al 2013].…”

Colour Helmholtz Stereopsis for Reconstruction of Complex Dynamic Scenes

“…In the recent work by Roubtsova and Guillemaut [8] Bayesian formulation of HS with a tailored prior jointly optimising depth and normal information is shown to produce superior results to the original ML formulation in [6]. There has also been some work [11], [12] on full 3D reconstruction by HS.…”