Three-dimensional range geometry compression via phase encoding

SCDeep: Single-Channel Depth Encoding for 3D-Range Geometry Compression Utilizing Deep-Learning Techniques

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.

Downsampled depth encoding for enhanced 3D range geometry compression

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.

State-of-the-art active optical techniques for three-dimensional surface metrology: a review [Invited]

This paper reviews recent developments of non-contact three-dimensional (3D) surface metrology using an active structured optical probe. We focus primarily on those active non-contact 3D surface measurement techniques that could be applicable to the manufacturing industry. We discuss principles of each technology, and its advantageous characteristics as well as limitations. Towards the end, we discuss our perspectives on the current technological challenges in designing and implementing these methods in practical applications.

Variable precision depth encoding for 3D range geometry compression

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.

Adaptive Binocular Fringe Dynamic Projection Method for High Dynamic Range Measurement

Three-dimensional measurement with fringe projection sensor has been commonly researched. However, the measurement accuracy and efficiency of most fringe projection sensors are still seriously affected by image saturation and the non-linear effects of the projector. In order to solve the challenge, in conjunction with the advantages of stereo vision technology and fringe projection technology, an adaptive binocular fringe dynamic projection method is proposed. The proposed method can avoid image saturation by adaptively adjusting the projection intensity. Firstly, the flowchart of the proposed method is explained. Then, an adaptive optimal projection intensity method based on multi-threshold segmentation is introduced to adjust the projection illumination. Finally, the mapping relationship of binocular saturation point and projection point is established by binocular transformation and left camera-projector mapping. Experiments demonstrate that the proposed method can achieve higher accuracy for high dynamic range measurement.

Two-channel depth encoding for 3D range geometry compression

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.

Three-dimensional range geometry compression via reduced entropy encoding of the image

The embedding approach of three-dimensional range geometry in two-dimensional images has led to a significant increase in the 3D-data compression ratio with respect to the image compression techniques development. With this approach, the main issue changed to maximum capacity utilization in the images, such as the number of color channels and bit depth. This was attained by reducing the number of interference patterns used as well as exploiting more powerful compression algorithms, such as free lossless image format. But this paper points out that compared to using conventional compression algorithms, modifying the mentioned process (two-dimensional image creation and compression) and finding the structure of the image data and its corresponding redundancy is preferable. Due to this effort, we will see a significant reduction in entropy of the information contained in the image, and this will create a new opportunity for improvement in lossless compression. Experimental evaluation and results verify the overall robustness of the proposed method. For example, a significant 0.29 bit per point ratio can be achieved for an object with a continuous smooth surface.

Active solution of homography for pavement crack recovery with four laser lines

An active solution method of the homography, which is derived from four laser lines, is proposed to recover the pavement cracks captured by the camera to the real-dimension cracks in the pavement plane. The measurement system, including a camera and four laser projectors, captures the projection laser points on the 2D reference in different positions. The projection laser points are reconstructed in the camera coordinate system. Then, the laser lines are initialized and optimized by the projection laser points. Moreover, the plane-indicated Plücker matrices of the optimized laser lines are employed to model the laser projection points of the laser lines on the pavement. The image-pavement homography is actively determined by the solutions of the perpendicular feet of the projection laser points. The pavement cracks are recovered by the active solution of homography in the experiments. The recovery accuracy of the active solution method is verified by the 2D dimension-known reference. The test case with the measurement distance of 700 mm and the relative angle of 8° achieves the smallest recovery error of 0.78 mm in the experimental investigations, which indicates the application potentials in the vision-based pavement inspection.