Disney Enterprises, Inc.

Eidgenossische Technische Hochschule Zurich

Samsung Electronics Co., Ltd.

Mario Botsch

Henning Zimmer

Do Kyoon Kim

Christopher Richard Schroers

Markus Gross

Jae Young Sim

Alexander Sorkine-Hornung

Changil Kim

Olga Sorkine-Hornung

Gael Guennebaud

Robert Carnecky

Katja Wolff

Alexander Sorkine Hornung

Kee Chang Lee

Federico Perazzi

Kae Chang Lee

Enhanced removing of noise and outliers from one or more point sets generated by image-based 3D reconstruction techniques is provided. In accordance with the disclosure, input images and corresponding depth maps can be used to remove pixels that are geometrically and/or photometrically inconsistent with the colored surface implied by the input images. This allows standard surface reconstruction methods (such as Poisson surface reconstruction) to perform less smoothing and thus achieve higher quality surfaces with more features. In some implementations, the enhanced point-cloud noise removal in accordance with the disclosure can include computing per-view depth maps, and detecting and removing noisy points and outliers from each per-view point cloud by checking if points are consistent with the surface implied by the other input views.

Point cloud noise and outlier removal for image-based 3D reconstruction

The disclosure provides an approach for image segmentation from an uncalibrated camera array. In one aspect, a segmentation application computes a pseudo depth map for each frame of a video sequence recorded with a camera array based on dense correspondences between cameras in the array. The segmentation application then fuses such pseudo depth maps computed for satellite cameras of the camera array to obtain a pseudo depth map at a central camera. Further, the segmentation application interpolates virtual green screen positions for an entire frame based on user input which provides control points and pseudo depth thresholds at the control points. The segmentation application then computes an initial segmentation based on a thresholding using the virtual green screen positions, and refines the initial segmentation by solving a binary labeling problem in a Markov random field to better align the segmentation with image edges and provide temporal coherency for the segmentation.

Video segmentation from an uncalibrated camera array

Provided is point-based efficient three-dimensional (3D) information representation from a color image that is obtained from a general Charge-Coupled Device (CCD)/Complementary Metal Oxide Semiconductor (CMOS) camera, and a depth image that is obtained from a depth camera. A 3D image processing method includes storing a depth image associated with an object as first data of a 3D data format, and storing a color image associated with the object as color image data of a 2D image format, independent of the first data.

Method and apparatus for processing three-dimensional (3D) images

Disclosed are data storing and processing methods or apparatuses which may efficiently perform a process of retrieving neighboring points between points stored in a leaf cell in a point-based 3D data expressing method. A computer-readable recording medium having stored thereon instructions for implementing a method of three-Dimensional (3D) data processing, the instructions including an instruction set of connecting each of a plurality of points with a leaf cell of a spatial tree structure; an instruction set of assigning a ball to correspond to each of the plurality of points; and an instruction set of connecting the ball with a leaf cell intersecting the ball.

3D image processing method and apparatus for enabling efficient retrieval of neighboring point

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Bielefeld, Germany

Mario Botsch