3D Point Cloud Processing

Robust Simultaneous 3D Registration via Rank Minimization

We present a robust and accurate 3D registration method for a dense sequence of depth images taken from unknown viewpoints. Our method simultaneously estimates multiple extrinsic parameters of the depth images to obtain a registered full 3D model of the scanned scene. By arranging the depth measurements in a matrix form, we formulate the problem as a simultaneous estimation of multiple extrinsics and a low-rank matrix, which corresponds to the aligned depth images as well as a sparse error matrix. Unlike previous approaches that use sequential or heuristic global registration approaches, our solution method uses an advanced convex optimization technique for obtaining a robust solution via rank minimization. To achieve accurate computation, we develop a depth projection method that has minimum sensitivity to sampling by reading projected depth values in the input depth images. We demonstrate the effectiveness of the proposed method through extensive experiments and compare it with previous standard techniques.

Thomas. D, Matsushita. Y and Sugimoto. A. Robust Simultaneous 3D Registration via Rank Minimization. In Proc. of The IEEE 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2012 Second International Conference on pp.33-40, 13-15 Oct. 2012

Illumination-Free Photometric Metric for Range Image Registration

This paper presents an illumination-free photometric metric for evaluating the goodness of a rigid transformation aligning two overlapping range images, under the assumption of Lambertian surface. Our metric is based on photometric re-projection error but not on feature detection and matching. We synthesize the color of one image using albedo of the other image to compute the photometric re-projection error. The unknown illumination and albedo are estimated from the correspondences induced by the input transformation using the spherical harmonics representation of image formation. This way allows us to derive an illumination-free photometric metric for range image alignment. We use a hypothesize-and-test method to search for the transformation that minimizes our illumination-free photometric function. Transformation candidates are efficiently generated by employing the spherical representation of each image. Experimental results using synthetic and real data show the usefulness of the proposed metric.

D. Thomas and A. Sugimoto. Photometric metric under unknown lighting for range image registration. In Transaction of Pattern Analysis and Machine Intelligence (TPAMI), 2013.

Robustly registering range images using local distribution of albedo

We propose a robust method for registering overlapping range images of a Lambertian object under a rough estimate of illumination. Because reflectance properties are invariant to changes in illumination, the albedo is promising to range image registration of Lambertian objects lacking in discriminative geometric features under variable illumination. We use adaptive regions in our method to model the local distribution of albedo, which enables us to stably extract the reliable attributes of each point against illumination estimates. We use a level-set method to grow robust and adaptive regions to define these attributes. A similarity metric between two attributes is also defined to match points in the overlapping area. Moreover, remaining mismatches are efficiently removed using the rigidity constraint of surfaces. Our experiments using synthetic and real data demonstrate the robustness and effectiveness of our proposed method.

Thomas. D and Sugimoto. A. Robustly registering range images using local distribution of albedo. In Computer Vision and Image Understanding (CVIU), Volume 28(4), Pages 649–667, Year 2011.