GASP : Geometric Association with Surface Patches

A fundamental challenge to sensory processing tasks in perception and robotics is the problem of obtaining data associations across views. We present a robust solution for ascertaining potentially dense surface patch (superpixel) associations, requiring just range information. Our approach involves decomposition of a view into regularized surface patches. We represent them as sequences expressing geometry invariantly over their superpixel neighborhoods, as uniquely consistent partial orderings. We match these representations through an optimal sequence comparison metric based on the Damerau-Levenshtein distance - enabling robust association with quadratic complexity (in contrast to hitherto employed joint matching formulations which are NP-complete). The approach is able to perform under wide baselines, heavy rotations, partial overlaps, significant occlusions and sensor noise. The technique does not require any priors -- motion or otherwise, and does not make restrictive assumptions on scene structure and sensor movement. It does not require appearance -- is hence more widely applicable than appearance reliant methods, and invulnerable to related ambiguities such as textureless or aliased content. We present promising qualitative and quantitative results under diverse settings, along with comparatives with popular approaches based on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201

Automatic Registration of RGBD Scans via Salient Directions

We address the problem of wide-baseline registration of RGB-D data, such as photo-textured laser scans without any artificial targets or prediction on the relative motion. Our approach allows to fully automatically register scans taken in GPS-denied environments such as urban canyon, industrial facilities or even indoors. We build upon image features which are plenty, localized well and much more discriminative than geometry features; however, they suffer from viewpoint distortions and request for normalization. We utilize the principle of salient directions present in the geometry and propose to extract (several) directions from the distribution of surface normals or other cues such as observable symmetries. Compared to previous work we pose no requirements on the scanned scene (like containing large textured planes) and can handle arbitrary surface shapes. Rendering the whole scene from these repeatable directions using an orthographic camera generates textures which are identical up to 2D similarity transformations. This ambiguity is naturally handled by 2D features and allows to find stable correspondences among scans. For geometric pose estimation from tentative matches we propose a fast and robust 2 point sample consensus scheme integrating an early rejection phase. We evaluate our approach on different challenging real world scenes