Modern hardware and algorithms, developed through several decades of research in multiple disciplines, are contributing toward the continuous advancement of 3D scanning technologies. Simply put, these technologies allow for the digital capture of an object's natural surface geometry. This article presents methods concerning the 3D scanning techniques using structured light, in which different patterns or structures are projected onto the scene to be scanned. The deformation of the original structure, as it falls upon the scanning scene, can be used to recover the scene's geometry. Within structured light technologies, there are several well‐developed approaches in the design of the pattern(s) that are cast upon a scene. Some of the approaches offer significant advantages, such as the ability to capture the 3D geometry of a scene at very high accuracies at real‐time rates. Given these advantages, the adoption of such 3D structured light technologies has been increasing. Being so, this article will also detail applications in which 3D structured light techniques are currently being employed within the fields of medicine, biology, manufacturing, security, communications, remote environment reconstruction, and consumer electronics.

Background: Shortages in qualified supervision and other resources prevent education personnel from rehearsing effective practices. Interactive simulations, although increasingly used in education, frequently require instructor management. Automated simulations rarely engage trainees in skills related to practice (eg, speech). Objective: We evaluated the capability of delivering behavioral skills training through an automated virtual reality (VR) simulation using artificial intelligence to improve the implementation of a nondirective mathematical questioning strategy. Methods: We recruited and randomly assigned 30 college-aged participants to equivalent treatment (ie, lecture, modeling, and VR; 15/30, 50%) and control groups (ie, lecture and modeling only; 15/30, 50%). The participants were blind to treatment conditions. Sessions and assessments were conducted face to face and involved the use of VR for assessment regardless of the condition. Lessons concerned the use of a nondirective mathematical questioning strategy in instances where a simulated student provided correct or incorrect answers to word problems. The measures included observed and automated assessments of participant performance and subjective assessments of participant confidence. The participants completed the pretest, posttest, and maintenance probes each week over the course of 3 weeks. Results: A mixed ANOVA revealed significant main effects of time (F2,27=124.154; P<.001; ηp2=0.816) and treatment (F1,28=19.281; P<.001; ηp2=0.408) as well as an interaction effect (F2,28=8.429; P<.001; ηp2=0.231) for the average percentage of steps in the questioning procedure. Posttest scores for the intervention group (mean 88%, SD 22.62%) exceeded those of the control group (mean 63.33%, SD 22.64%), with t28=3.653, P<.001, and Cohen d=1.334. Maintenance scores indicated a positive effect of the intervention (mean 83.33%, SD 24.40%) relative to the control (mean 54.67%, SD 15.98%), t28=3.807, P<.001, Cohen d=1.39. A Mann-Whitney U test indicated that the treatment groups’ self-ratings of confidence (mean 2.41, SD 0.51) were higher than those of the control group (mean 2.04, SD 0.52), U=64, P=.04, r=0.137. Conclusions: The results demonstrate the potential of artificial intelligence-augmented VR to deliver effective, evidence-based training with limited instructor management. Additional work is needed to demonstrate the cascading effect of training on authentic practice and to encompass a wider range of skills.

This paper presents a novel method for representing three-dimensional (3D) range data within regular two-dimensional (2D) images using multiwavelength encoding. These 2D images can then be further compressed using traditional lossless (e.g., PNG) or lossy (e.g., JPEG) image compression techniques. Current 3D range data compression methods require significant filtering to reduce lossy compression artifacts. The nature of the proposed encoding, however, offers a significant level of robustness to such artifacts brought about by high levels of JPEG compression. This enables extremely high compression ratios while maintaining a very low reconstruction error percentage with little to no filtering required to remove compression artifacts. For example, when encoding 3D geometry with the proposed method and storing the resulting 2D image with Matlab R2014a JPEG80 image compression, compression ratios of approximately 935:1 versus the OBJ format can be achieved at an error rate of approximately 0.027% without any filtering.

Decade-long research efforts toward superfast three-dimensional (3-D) shape measurement leveraging the digital micromirror device (DMD) platforms are summarized. Specifically, we will present the following technologies: (1) high-resolution real-time 3-D shape measurement technology that achieves 30 Hz simultaneous 3-D shape acquisition, reconstruction, and display with more than 300,000 points per frame; (2) superfast 3-D optical metrology technology that achieves 3-D measurement at a rate of tens of kilohertz utilizing the binary defocusing method we invented; and (3) the improvement of the binary defocusing technology for superfast and high-accuracy 3-D optical metrology using the DMD platforms. Both principles and experimental results are presented.

Recent world events have caused a dramatic rise in the use of video conferencing solutions such as Zoom and FaceTime. Although 3D capture and display technologies are becoming common in consumer products (e.g., Apple iPhone TrueDepth sensors, Microsoft Kinect devices, and Meta Quest VR headsets), 3D telecommunication has not yet seen any appreciable adoption. Researchers have made great progress in developing advanced 3D telepresence systems, but often with burdensome hardware and network requirements. In this work, we present HoloKinect, an open-source, user-friendly, and GPU-accelerated platform for enabling live, two-way 3D video conferencing on commodity hardware and a standard broadband internet connection. A Microsoft Azure Kinect serves as the capture device and a Looking Glass Portrait multiscopically displays the final reconstructed 3D mesh for a hologram-like effect. HoloKinect packs color and depth information into a single video stream, leveraging multiwavelength depth (MWD) encoding to store depth maps in standard RGB video frames. The video stream is compressed with highly optimized and hardware-accelerated video codecs such as H.264. A search of the depth and video encoding parameter space was performed to analyze the quantitative and qualitative losses resulting from HoloKinect’s lossy compression scheme. Visual results were acceptable at all tested bitrates (3–30 Mbps), while the best results were achieved with higher video bitrates and full 4:4:4 chroma sampling. RMSE values of the recovered depth measurements were low across all settings permutations.