No abstract

Figure 1: Floating Scale Surface Reconstruction example. 6 out of 384 input images of a multi-scale dataset (left). Registered images are processed with multi-view stereo which yields depth maps with drastically different sampling rates of the surface. Our algorithm is able to accurately reconstruct every caputured detail of the dataset using a novel multi-scale reconstruction approach (right). AbstractAny sampled point acquired from a real-world geometric object or scene represents a finite surface area and not just a single surface point. Samples therefore have an inherent scale, very valuable information that has been crucial for high quality reconstructions. We introduce a new method for surface reconstruction from oriented, scale-enabled sample points which operates on large, redundant and potentially noisy point sets. The approach draws upon a simple yet efficient mathematical formulation to construct an implicit function as the sum of compactly supported basis functions. The implicit function has spatially continuous "floating" scale and can be readily evaluated without any preprocessing. The final surface is extracted as the zero-level set of the implicit function. One of the key properties of the approach is that it is virtually parameter-free even for complex, mixed-scale datasets. In addition, our method is easy to implement, scalable and does not require any global operations. We evaluate our method on a wide range of datasets for which it compares favorably to popular classic and current methods.

We present a photometric stereo technique that operates on time-lapse sequences captured by static outdoor webcams over the course of several months. Outdoor webcams produce a large set of uncontrolled images subject to varying lighting and weather conditions. We first automatically select a suitable subset of the captured frames for further processing, reducing the dataset size by several orders of magnitude. A camera calibration step is applied to recover the camera response function, the absolute camera orientation, and to compute the light directions for each image. Finally, we describe a new photometric stereo technique for non-Lambertian scenes and unknown light source intensities to recover normal maps and spatially varying materials of the scene

Multi-view stereo systems can produce depth maps with large variations in viewing parameters, yielding vastly different sampling rates of the observed surface. We present a new method for surface reconstruction by integrating a set of registered depth maps with dramatically varying sampling rate. The method is based on the construction of a hierarchical signed distance field represented in an incomplete primal octree by incrementally adding triangulated depth maps. Due to the adaptive data structure, our algorithm is able to handle depth maps with varying scale and to consistently represent coarse, low-resolution regions as well as small details contained in high-resolution depth maps. A final surface mesh is extracted from the distance field by construction of a tetrahedral complex from the scattered signed distance values and applying the Marching Tetrahedra algorithm on the partition. The output is an adaptive triangle mesh that seamlessly connects coarse and highly detailed regions while avoiding filling areas without suitable input data

View interpolation and image-based rendering algorithms often produce visual artifacts in regions where the 3D scene geometry is erroneous, uncertain, or incomplete. We introduce ambient point clouds constructed from colored pixels with uncertain depth, which help reduce these artifacts while providing non-photorealistic background coloring and emphasizing reconstructed 3D geometry. Ambient point clouds are created by randomly sampling colored points along the viewing rays associated with uncertain pixels. Our realtime rendering system combines these with more traditional rigid 3D point clouds and colored surface meshes obtained using multiview stereo. Our resulting system can handle larger-range view transitions with fewer visible artifacts than previous approaches.