2020

"Variable precision depth encoding for 3D range geometry compression," Appl. Opt. (2020)

M.G. Finley, J.Y. Nishimura, and T. Bell, “Variable precision depth encoding for 3D range geometry compression,” Appl. Opt., 59(17), 5290-5299, 2020.

Abstract

State-of-the-art 3D range geometry compression algorithms that utilize principles of phase shifting perform encoding with a fixed frequency; therefore, it is not possible to encode individual points within a scene at various degrees of precision. This paper presents a novel, to the best of our knowledge, method for accurately encoding 3D range geometry within the color channels of a 2D RGB image that allows the encoding frequency—and therefore the encoding precision—to be uniquely determined for each coordinate. The proposed method can thus be used to balance between encoding precision and file size by encoding geometry along a statistical distribution. For example, a normal distribution allows for more precise encoding where the density of data is high and less precise encoding where the density of data is low. Alternative distributions may be followed to produce encodings optimized for specific applications. In general, the nature of the proposed encoding method enables the precision to be freely controlled at each point or centered around identified features of interest, ideally enabling this method to be used within a wide range of applications.

"Variable precision depth encoding for 3D range geometry compression," Electronic Imaging, 3DMP (2020)

EI2020_Banner4Web_1024x102.png

M.G. Finley and T.Bell, “Variable precision depth encoding for 3D range geometry compression,” Electronic Imaging, 3D Measurement and Data Processing (3DMP), Burlingame, CA, Jan. 2020. (Accepted, Peer Reviewed)

Abstract

This paper presents a novel method for accurately encoding 3D range geometry within the color channels of a 2D RGB image that allows the encoding frequency—and therefore the encoding precision—to be uniquely determined for each coordinate. The proposed method can thus be used to balance between encoding precision and file size by encoding geometry along a normal distribution; encoding more precisely where the density of data is high and less precisely where the density is low. Alternative distributions may be followed to produce encodings optimized for specific applications. In general, the nature of the proposed encoding method is such that the precision of each point can be freely controlled or derived from an arbitrary distribution, ideally enabling this method for use within a wide range of applications

To be presented at Electronic Imaging 2020. Full paper will be posted at this time.