RESUMO
The use of three-dimensional (3D) range geometry is expanding across a variety of disciplines ranging from medicine to the visual arts. A large amount of information is available in 3D range geometry, causing some applications to be limited in their ability to effectively store or transmit captured data. To help alleviate this constraint, a variety of 3D range data compression techniques have been proposed. One method, multiwavelength depth (MWD) encoding, smoothly encodes 3D range geometry into the three color channels of a 2D RGB image. To the best of our knowledge, we present a novel compression enhancement to further reduce file sizes that employs image downsampling, MWD encoding, and lossless (e.g., PNG) or lossy (e.g., JPEG) compression. Image upsampling is used to return downsampled encodings to their original resolution from which the 3D information is then decoded. The proposed method is robust to various scales of downsampling and levels of lossy compression. For example, when this method was applied with 50% downsampling and JPEG 85 to an encoding of a 3D face scan, a compression ratio of 68.85:1 versus the raw data was achieved with a global RMS reconstruction accuracy of 98.77%. Experimental results demonstrate that the proposed method can provide substantial file size savings at minimal reduction in overall reconstruction accuracy.
RESUMO
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.