| Summary: | Recent advances in optics and computing technologies have encouraged many applications to adopt the use of three-dimensional (3D) data for the measurement and visualization of the world around us. Modern 3D-range scanning systems have become much faster than real-time and are able to capture data with incredible precision. However, increasingly fast acquisition speeds and high fidelity data come with increased storage and transmission costs. In order to enable applications that wish to utilize these technologies, efforts must be made to compress the raw data into more manageable formats. One common approach to compressing 3D-range geometry is to encode its depth information within the three color channels of a traditional 24-bit RGB image. To further reduce file sizes, this paper evaluates two novel approaches to the recovery of floating-point 3D range data from only a single-channel 8-bit image using machine learning techniques. Specifically, the recovery of depth data from a single channel is enabled through the use of both semantic image segmentation and end-to-end depth synthesis. These two distinct approaches show that machine learning techniques can be utilized to enable significant file size reduction while maintaining reconstruction accuracy suitable for many applications. For example, a complex set of depth data encoded using the proposed method, stored in the JPG 20 format, and recovered using semantic segmentation techniques was able to achieve an average RMS reconstruction accuracy of 99.18% while achieving an average compression ratio of 106:1 when compared to the raw floating-point data. When end-to-end synthesis techniques were applied to the same encoded dataset, an average reconstruction accuracy of 99.59% was experimentally demonstrated for the same average compression ratio. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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