ABSTRACT
The capacity of three-dimensional (3D) range geometry acquisition methods to capture high-precision scans at high frame rates increases every year. These improvements have influenced a broadening range of disciplines to implement 3D range geometry capture systems, including telepresence, medicine, the visual arts, and many others. However, its increased popularity, precision, and capture rates have caused mounting pressure on the storage and transmission of 3D range geometry, thus straining their capacities. Compression techniques seek to alleviate this pressure by offering reduced file sizes, while maintaining the levels of precision needed for particular applications. Several such compression methods use sinusoidal modulation approaches to encode floating-point 3D data into conventional 2D red, green, and blue (RGB) images. In some applications, such as telepresence, high precision may only be required in a particular region within a depth scan, thus allowing less important data to be compressed more aggressively. This paper proposes a feature-driven compression method that provides a way to encode regions of interest at higher levels of precision while encoding the remaining data less precisely to reduce file sizes. This method supports both lossless and lossy compression, enabling even greater file-size savings. For example, in the case of a depth scan of a bust, an algorithmically extracted bounding box of the face was used to create a foveated encoding distribution so that the facial region was encoded at higher precisions. When using JPEG 80, the RMS reconstruction error of this novel, to the best of our knowledge, encoding was 0.56 mm in the region of interest, compared to a globally fixed higher precision encoding where the error was 0.54 mm in the same region. However, the proposed encoding achieved a 26% reduction in overall compressed file size compared to the fixed, higher-precision encoding.
Subject(s)
Data Compression , Data Compression/methodsABSTRACT
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.
ABSTRACT
This paper presents a novel method for accurately encoding 3D range geometry within only two channels of a 2D RGB image using a two-frequency phase-shifting approach. Once encoded within a 2D image, 3D geometry can be further compressed with conventional lossless or lossy image compression methods. The nature of the proposed two-channel encoding is relatively smooth; thus, large compression ratios with high reconstruction accuracies can be achieved and are experimentally demonstrated. For example, a compression ratio of 2883:1 was achieved, compared with the STL format, with a reconstruction RMS error of 0.45 mm (99.8% accuracy) when JPEG 85 was used with the proposed method. This paper also demonstrates how a 24-bit color texture map can be encoded alongside 3D geometry within a single 2D image.
