Depth Distortion Research Articles

Depth distortion and angular deviation of a fully guided tooth-supported static surgical guide in a partially edentulous patient: A systematic review and meta-analysis.

This systematic review and meta-analysis aimed to evaluate the depth distortion and angular deviation of fully-guided tooth-supported static surgical guides (FTSG) in partially edentulous arches compared to partially guided surgical guides or freehand. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered in the Open Science Framework (OSF). The formulated population, intervention, comparison, and outcome (PICO) question was: "In partially edentulous arches, what are the depth distortion and angular deviation of FTSG compared to partially guided surgical guides or freehand?" The search strategy involved four main electronic databases, and an additional manual search was completed in November 2023 by following an established search strategy. Initial inclusion was based on titles and abstracts, followed by a detailed review of selected studies, and clinical studies that evaluated the angular deviations or depth distortion in FTSG in partial arches, compared to partially guided surgical guides or freehand, were included. In FTSG, two surgical approaches were compared: open flap and flapless techniques, and two digital methods were assessed for surgical guide design with fiducial markers or dental surfaces. A qualitative analysis for clinical studies was used to assess the risk of bias. The certainty of the evidence was assessed according to the grading of recommendations, assessment, development, and evaluations (GRADE) system. In addition, a single-arm meta-analysis of proportion was performed to evaluate the angular deviation of freehand and FTSG. Ten studies, published between 2018 and 2023, met the eligibility criteria. Among them, 10 studies reported angular deviations ranging from -0.32° to 4.96° for FTSG. Regarding FTSG surgical approaches, seven studies examined the open flap technique for FTSG, reporting mean angular deviations ranging from 2.03° to 4.23°, and four studies evaluated flapless FTSG, reporting angular deviations ranging from -0.32° to 3.38°. Six studies assessed the freehand surgical approach, reporting angular deviations ranging from 1.40° to 7.36°. The mean depth distortion ranged between 0.19mm to 2.05mm for open flap FTSG, and between 0.15mm to 0.45mm for flapless FTSG. For partially guided surgical guides, two studies reported angular deviations ranging from 0.59° to 3.44°. Seven studies were eligible for meta-analysis, focusing on the FTSG in open flap technique, with high heterogeneity (I2 (95%CI) = 92.3% (88.7%-96.4%)). In contrast, heterogeneity was low in studies comparing freehand versus FTSG in open flap techniques (I2 (95%CI) = 21.3% (0.0%-67.8%)), favoring the FTSG surgical approach. In partially edentulous arches, FTSG systems exhibited less angular deviation than freehand and partially guided surgical guides. Flapless surgical approaches were associated with reduced angular deviation and depth distortion, suggesting a potential preference for the FTSG method in these procedures.

The geometry of the vergence-accommodation conflict in mixed reality systems

Mixed reality technologies, such as virtual (VR) and augmented (AR) reality, present promising opportunities to advance education and professional training due to their adaptability to diverse contexts. Distortions in the perceived distance in such mediated conditions, however, are well documented and have imposed nontrivial challenges that complicate and limit transferring task performance in a virtual setting to the unmediated reality (UR). One potential source of the distance distortion is the vergence-accommodation conflict—the discrepancy between the depth specified by the eyes’ accommodative state and the angle at which the eyes converge to fixate on a target. The present study involved the use of a manual pointing task in UR, VR, and AR to quantify the magnitude of the potential depth distortion in each modality. Conceptualizing the effect of vergence-accommodation offset as a constant offset to the vergence angle, a model was developed based on the stereoscopic viewing geometry. Different versions of the model were used to fit and predict the behavioral data for all modalities. Results confirmed the validity of the conceptualization of vergence-accommodation as a device-specific vergence offset, which predicted up to 66% of the variance in the data. The fitted parameters indicate that, due to the vergence-accommodation conflict, participants’ vergence angle was driven outwards by approximately 0.2°, which disrupted the stereoscopic viewing geometry and produced distance distortion in VR and AR. The implications of this finding are discussed in the context of developing virtual environments that minimize the effect of depth distortion.

BiPMAP: a toolbox for predicting perceived motion artifacts on modern displays.

Viewers of digital displays often experience motion artifacts (e.g., flicker, judder, edge banding, motion blur, color breakup, depth distortion) when presented with dynamic scenes. We developed an interactive software tool for display designers that predicts how a viewer perceives motion artifacts for a variety of stimulus, display, and viewing parameters: the Binocular Perceived Motion Artifact Predictor (BiPMAP). The tool enables the user to specify numerous stimulus, display, and viewing parameters. It implements a model of human spatiotemporal contrast sensitivity in order to determine which artifacts will be seen by a viewer and which will not. The tool visualizes the perceptual effects of discrete space-time sampling on the display by presenting side by side the expected perception when the stimulus is continuous compared to when the same stimulus is presented with the spatial and temporal parameters of a prototype display.

The Effect of Stereoscopic Depth Distortion on the Near Oculomotor Response

The Effect of Stereoscopic Depth Distortion on the Near Oculomotor Response

Jitter noise modeling and its removal using recursive least squares in shape from focus systems

Three-dimensional shape recovery from the set of 2D images has many applications in computer vision and related fields. Passive techniques of 3D shape recovery utilize a single view point and one of these techniques is Shape from Focus or SFF. In SFF systems, a stack of images is taken with a single camera by manipulating its focus settings. During the image acquisition, the inter-frame distance or the sampling step size is predetermined and assumed constant. However, in a practical situation, this step size cannot remain constant due to mechanical vibrations of the translational stage, causing jitter. This jitter produces Jitter noise in the resulting focus curves. Jitter noise is invisible in every image, because all images in the stack are exposed to the same error in focus; thus, limiting the use of traditional noise removal techniques. This manuscript formulates a model of Jitter noise based on Quadratic function and the Taylor series. The proposed method, then, solves the jittering problem for SFF systems through recursive least squares (RLS) filtering. Different noise levels were considered during experiments performed on both real as well as simulated objects. A new metric measure is also proposed, referred to as depth distortion (DD), which calculates the number of pixels contributing to the RMSE in percentage. The proposed measure is used along with the RMSE and correlation, to compute and test the reconstructed shape quality. The results confirm the effectiveness of the proposed scheme.

A light field three-dimensional ranging system based on regularization

A high-precision light field three-dimensional ranging system based on regularization is proposed. This method transforms the sub-aperture image resolution problem into a global optimization problem. The objective function is constructed to capture the relationships between the light field views. The complementary information in the light field views is then utilized to improve the spatial resolution of the light field. In particular, regularizations of fuzzy matrix-based and optical flow-field-based use of information from multiple viewpoints can effectively enhance the light field structure of the system. The experimental results show that the proposed method can perform scene distance measurement accuracy and has better depth estimation. The proposed method effectively solves image depth distortion in traditional light field imaging. It eliminates the influence of low resolution of sub-aperture images on depth estimation to the maximum extent.

Depth distortion correction for consumer-grade depth cameras in crop reconstruction

Modern consumer-grade RGB-D cameras provide intensive depth estimation and high frame rates. They have been widely used in agriculture. However, depth anamorphose occurs when using the RGB-D camera. In order to address this issue, this paper proposes a novel approach to correct the distorted depth images that fit the relationship between the true distances and the depth values from depth images. This study considers the structured light camera ASUS Xtion PRO LIVE as example to develop a system for obtaining a series of depth images at different distances from the target plane. A comparison analysis is conducted between the images before and after correction to evaluate the performance of the distortion correction. The point cloud image of corrected maize leaves is well coincident with the original plant. This approach improves the accuracy of depth measurement and optimizes the subsequent use of the depth camera in crop reconstruction and phenotyping studies.

Stereo Seam Coupling and Depth Distortion Score in 3D Image Retargeting Using DMA Algorithm

Preserving the highlighted contents in left and right stereo images by using seam carving technique is the challenging job in current digital era. The seam carving technique facilitates to minimise the trouble by eliminating unwanted seam patches from the stereo images by using saliency detection method. These images are popularly known as stereoscopic images. This paper addresses the issues in 2-D planer images and emphasised on 3-D stereoscopic images. We method enforces on the foreground pixels which are highly considerable by calculating the high energy pixels. The depth distortion estimation is performed by adjusting the aspect ratio. The viewpoints of left and right stereo images are somewhat different. Disparity Map Acquisition (DMA) algorithm maps the pixel points from the left stereo image to its corresponding right image pixels. The fusion of the left and right images is incorporating to minimise the depth distortion score.

CIRO: The Effects of Visually Diminished Real Objects on Human Perception in Handheld Augmented Reality

Augmented reality (AR) scenes often inadvertently contain real world objects that are not relevant to the main AR content, such as arbitrary passersby on the street. We refer to these real-world objects as content-irrelevant real objects (CIROs). CIROs may distract users from focusing on the AR content and bring about perceptual issues (e.g., depth distortion or physicality conflict). In a prior work, we carried out a comparative experiment investigating the effects on user perception of the AR content by the degree of the visual diminishment of such a CIRO. Our findings revealed that the diminished representation had positive impacts on human perception, such as reducing the distraction and increasing the presence of the AR objects in the real environment. However, in that work, the ground truth test was staged with perfect and artifact-free diminishment. In this work, we applied an actual real-time object diminishment algorithm on the handheld AR platform, which cannot be completely artifact-free in practice, and evaluated its performance both objectively and subjectively. We found that the imperfect diminishment and visual artifacts can negatively affect the subjective user experience.

Stereoscopic Image Retargeting Based on Deep Convolutional Neural Network

Stereoscopic image retargeting aims at converting stereoscopic images to the target resolution adaptively. Different from 2D image retargeting, stereoscopic image retargeting needs to preserve both the shape structure of salient objects and depth consistency of 3D scenes. In this paper, we present a stereoscopic image retargeting method based on deep convolutional neural network to obtain high-quality retargeted images with both object shape preservation and scene depth preservation. First, a cross-attention extraction mechanism is constructed to generate attention map, which contains the valuable attention features of the left and right images and the common attention features between them. Second, since the disparity map can provide accurate depth information of objects in 3D scenes, a disparity-assisted 3D significance map generation module is utilized to further preserve the valuable depth information of stereoscopic images. Finally, in order to predict the retargeted stereoscopic images accurately, an image consistency loss is developed to preserve the geometric structure of salient objects, and a disparity consistency loss is introduced to eliminate depth distortions. Experimental results demonstrate that the proposed deep convolutional neural network can provide favorable stereoscopic image retargeting results.

Depth Trajectory-Aware Stereoscopic Video Retargeting

The image/video retargeting (resolution adaptation) problem—adaption of source data (image/video) to different screen resolution—has garnered wide attention due to compelling applications in intelligent adaptive display. Most of the current image/video displaying application achieves the adaption through scaling source uniformly. Compared with traditional image/video retargeting, stereoscopic video retargeting poses new challenges that should preserve shape and depth of salient objects with temporal coherence. In this paper, we propose a depth trajectory-aware stereoscopic video retargeting method by imposing constraints to optimize the coordinates and depths simultaneously. To reduce visual discomfort induced by fast depth motion, our method uses a temporal depth distortion energy to optimize the depth trajectory in temporal direction. As a result, our solution can preserve shape, depth and temporal fidelity of salient objects simultaneously with comfortable visual experience.

Highly Efficient Multiview Depth Coding Based on Histogram Projection and Allowable Depth Distortion.

Mismatches between the precisions of representing the disparity, depth value and rendering position in 3D video systems cause redundancies in depth map representations. In this paper, we propose a highly efficient multiview depth coding scheme based on Depth Histogram Projection (DHP) and Allowable Depth Distortion (ADD) in view synthesis. Firstly, DHP exploits the sparse representation of depth maps generated from stereo matching to reduce the residual error from INTER and INTRA predictions in depth coding. We provide a mathematical foundation for DHP-based lossless depth coding by theoretically analyzing its rate-distortion cost. Then, due to the mismatch between depth value and rendering position, there is a many-to-one mapping relationship between them in view synthesis, which induces the ADD model. Based on this ADD model and DHP, depth coding with lossless view synthesis quality is proposed to further improve the compression performance of depth coding while maintaining the same synthesized video quality. Experimental results reveal that the proposed DHP based depth coding can achieve an average bit rate saving of 20.66% to 19.52% for lossless coding on Multiview High Efficiency Video Coding (MV-HEVC) with different groups of pictures. In addition, our depth coding based on DHP and ADD achieves an average depth bit rate reduction of 46.69%, 34.12% and 28.68% for lossless view synthesis quality when the rendering precision varies from integer, half to quarter pixels, respectively. We obtain similar gains for lossless depth coding on the 3D-HEVC, HEVC Intra coding and JPEG2000 platforms.

Experimental Evaluation of Distortion in Amplitude Modulation Techniques for Parametric Loudspeakers

Parametric loudspeakers can generate a highly directional beam of sound, having applications in targeted audio delivery. Audible sound modulated into an ultrasonic carrier will get self-demodulated along the highly directive beam due to the non-linearity of air. This non-linear demodularization should be compensated to reduce audio distortion, different amplitude modulation techniques have been developed during the last years. However, some studies are only theoretical whereas others do not analyze the audio distortion in depth. Here, we present a detailed experimental evaluation of the frequency response, harmonic distortion and intermodulation distortion for various amplitude modulation techniques applied with different indices of modulation. We used a simple method to measure the audible signal that prevents the saturation of the microphones when the high levels of the ultrasonic carrier are present. This work could be useful for selecting predistortion techniques and indices of modulation for regular parametric arrays.

Perceptual Temporal Incoherence-Guided Stereo Video Retargeting.

Stereo video retargeting aims at minimizing shape and depth distortions with temporal coherence in resizing a stereo video content to a desired size. Existing methods extend stereo image retargeting schemes to stereo video retargeting by adding additional temporal constraints that demand temporal coherence in all corresponding regions. However, such a straightforward extension incurs conflicts among multiple requirements (i.e., shape and depth preservation and their temporal coherence), thus failing to meet one or more of these requirements satisfactorily. To mitigate conflicts among depth, shape, and temporal constraints and avoid degrading temporal coherence perceptually, we relax temporal constraints for non-paired regions at frame boundaries, derive new temporal constraints to improve human viewing experience of a 3D scene, and propose an efficient grid-based implementation for stereo video retargeting. Experimental results demonstrate that our method achieves superior visual quality over existing methods.

Performance Evaluation of Mixed Reality Display for Guidance During Transcatheter Cardiac Mapping and Ablation.

Cardiac electrophysiology procedures present the physician with a wealth of 3D information, typically presented on fixed 2D monitors. New developments in wearable mixed reality displays offer the potential to simplify and enhance 3D visualization while providing hands-free, dynamic control of devices within the procedure room. Objective: This work aims to evaluate the performance and quality of a mixed reality system designed for intraprocedural use in cardiac electrophysiology. Method: The Enhanced Electrophysiology Visualization and Interaction System (ĒLVIS) mixed reality system performance criteria, including image quality, hardware performance, and usability were evaluated using existing display validation procedures adapted to the electrophysiology specific use case. Additional performance and user validation were performed through a 10 patient, in-human observational study, the Engineering ĒLVIS (E2) Study. Results: The ĒLVIS system achieved acceptable frame rate, latency, and battery runtime with acceptable dynamic range and depth distortion as well as minimal geometric distortion. Bench testing results corresponded with physician feedback in the observational study, and potential improvements in geometric understanding were noted. Conclusion: The ĒLVIS system, based on current commercially available mixed reality hardware, is capable of meeting the hardware performance, image quality, and usability requirements of the electroanatomic mapping display for intraprocedural, real-time use in electrophysiology procedures. Verifying off the shelf mixed reality hardware for specific clinical use can accelerate the adoption of this transformative technology and provide novel visualization, understanding, and control of clinically relevant data in real-time.

Stereoscopic 3D Optic Flow Distortions Caused by Mismatches between Image Acquisition and Display Parameters.

We analyze the impact of common stereoscopic 3D (S3D) depth distortion on S3D optic flow in virtual reality (VR) environments. The depth distortion is introduced by mismatches between the image acquisition and display parameter. The results show that such S3D distortions induce large S3D optic flow distortions and may even induce partial/full optic flow reversal within a certain depth range, depending on the viewer's moving speed and the magnitude of S3D distortion introduced. We hypothesize that the S3D optic flow distortion may be a source of intra-sensory conflict that may be a source of visually induced motion sickness (VIMS) in S3D.

Performance Analysis of the PMD Camboard Picoflexx Time-of-Flight Camera for Markerless Motion Capture Applications

The PMD Camboard Picoflexx Time-of-Flight (ToF) camera is evaluated against the Microsoft Kinect V2 to assess its performance in the context of markerless motion capture system for human body kinematics measurements. Various error sources such as the warm-up time, the depth distortion, the amplitude related error, the signal-to-noise ratio, and limitations such as their dependence on the illumination pattern and on the target distance, are studied and compared. The Picoflexx device is also compared to the Kinect V2 in relation to the quality of shape reconstructions, to assess its adequateness in modeling human body segments, and human body kinematics measurements. The final result of this paper is definitely useful to the research community, demonstrating that, even if the Picoflexx performs lower than the Kinect concerning the measurement performances, its behavior in estimating the volume of the body segments, the angles at the joints for human body kinematics, and the angle at the ankle in assisted walking applications is definitely satisfactory. These results are extremely significant to obtain accurate estimates of the parameters of the human body models in markerless motion capture applications, especially in laboratory-free environments, where compactness, lightweight, wireless connection, and low-power consumption are of outmost importance.

Rate-Distortion Optimal Joint Texture and Depth Map Coding for 3-D Video Streaming

For high compression efficiency, 3-D video coding usually employs a multimode methodology to exploit the dependencies between multiple views as well as between texture and depth. However, different coding modes will posses differentiating error propagation behaviour when the compressed 3-D video bit stream is transmitted over packet-switched networks, and thus lead to different amount of visual distortions. Further, the texture and depth distortions are combined in a highly complex fashion to produce the overall view synthesis distortion. To minimize the expected view synthesis distortion, this paper proposes an efficient rate-distortion optimized algorithm for joint selection of texture and depth modes. Firstly, a statistical model is developed to estimate the overall view synthesis distortion, in which the channel distortions caused by error propagation under different coding modes are analyzed. Then, joint optimization of texture and depth modes is derived within an operational rate-distortion framework using the Lagrange multiplier method. The adjacent block dependency caused by warping operation is explicitly considered in optimization, for which we develop a dynamic programming method to find the optimal solution. Finally, we extend the Lagrange minimization method to the more general variable-block-size prediction case, where the optimal quadtree tree structure and the combined coding modes are jointly determined using a multi-level dual trellis. Experimental results are presented for a wide range of packet loss rates to illustrate the effectiveness of the proposed algorithm.

No-reference quality metric for HEVC compression distortion estimation in depth maps

Multiview video plus depth (MVD) is the most popular 3D video format due to its efficient compression and provision for novel view generation enabling the free-viewpoint applications. In addition to color images, MVD format provides depth maps which are exploited to generate intermediate virtual views using the depth image-based rendering (DIBR) techniques. Compression affects the quality of the depth maps which in turn may introduce various structural and textural distortions in the DIBR-synthesized images. Estimation of the compression-related distortion in depth maps is very important for a high-quality 3D experience. The task becomes challenging when the corresponding reference depth maps are unavailable, e.g., when evaluating the quality on the decoder side. In this paper, we present a no-reference quality assessment algorithm to estimate the distortion in the depth maps induced by compression. The proposed algorithm exploits the depth saliency and local statistical characteristics of the depth maps to predict the compression distortion. The proposed ‘depth distortion evaluator’ (DDE) is evaluated on depth videos from standard MVD database compressed with the state-of-the-art high-efficiency video coding at various quality levels. The results demonstrate that DDE can be used to effectively estimate the compression distortion in depth videos.

Unfocused plenoptic metric modeling and calibration.

For unfocused plenoptic imaging systems, metric calibration is generally mandatory to achieve high-quality imaging and metrology. In this paper, we present an explicit derivation of an unfocused plenoptic metric model associating a measured light field in the object space with a recorded light field in the image space to conform physically to the imaging properties of unfocused plenoptic cameras. In addition, the impact of unfocused plenoptic imaging distortion on depth computation was experimentally explored, revealing that radial distortion parameters contain depth-dependent common factors, which were then modeled as depth distortions. Consequently, a complete unfocused plenoptic metric model was established by combining the explicit metric model with the imaging distortion model. A three-step unfocused plenoptic metric calibration strategy, in which the Levenberg-Marquardt algorithm is used for parameter optimization, is correspondingly proposed to determine 12 internal parameters for each microlens unit. Based on the proposed modeling and calibration, the depth measurement precision can be increased to 0.25 mm in a depth range of 300 mm, ensuring the potential applicability of consumer unfocused plenoptic cameras in high-accuracy three-dimensional measurement.