Hierarchical sparse coded surface models

In this paper, we describe a novel approach to construct textured 3D environment models in a hierarchical fashion based on local surface patches. Compared to previous approaches, the hierarchy enables our method to represent the environment with differently sized surface patches. The reconstruction scheme starts at a coarse resolution with large patches and in an iterative fashion uses the reconstruction error to guide the decision as to whether the resolution should be refined. This leads to variable resolution models that represent areas with few variations at low resolution and areas with large variations at high resolution. In addition, we compactly describe local surface attributes via sparse coding based on an overcomplete dictionary. In this way, we additionally exploit similarities in structure and texture, which leads to compact models. We learn the dictionary directly from the input data and independently for every level in the hierarchy in an unsupervised fashion. Practical experiments with large-scale datasets demonstrate that our method compares favorably with two state-of-the-art techniques while being comparable in accuracy.