Absolute Depth Research Articles

Absolute depth resolved optic axis measurement with catheter-based polarization sensitive optical coherence tomography

Absolute depth resolved optic axis measurement with catheter-based polarization sensitive optical coherence tomography

Ordinal information, but not metric information, matters in binding feature with depth location in three-dimensional contexts.

A basic function of human visual perception is the ability to recognize and locate objects in the environment. It has been shown that two-dimensional (2D) location can reliably bias judgments on object identity (spatial congruency bias; Golomb et al., 2014), suggesting that 2D location information is automatically bound with object features to induce such a bias. Although the binding problem of feature and location has been vigorously studied under various 2D settings, it remains unclear how depth location can be bound with object features in a three-dimensional (3D) setting. Here we conducted five experiments in various 3D contexts using the congruency bias paradigm, and found that changes of object's depth location could influence perceptual judgments on object features differently depending on whether its relative depth order with respect to others changed or not. Experiments 1 and 2 showed that the judgments on an object's color could be affected by changes in its ordinal depth, but not by changes in its absolute metric depth. Experiment 3 showed that the bias was asymmetric-changes in an object's color did bias the judgments on metric-depth location, but not if its depth order had changed. Experiments 4 and 5 investigated whether these findings could be generalized to a peripersonal near space and a large-scale far space, respectively, using more ecological virtual environments. Our findings suggest that ordinal depth plays a special role in feature-location binding: an object may be automatically bound with its relative depth relation, but not with its absolute metric-depth location. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Depth Perception in Optical See-Through Augmented Reality: Investigating the Impact of Texture Density, Luminance Contrast, and Color Contrast.

The immersive augmented reality (AR) system necessitates precise depth registration between virtual objects and the real scene. Prior studies have emphasized the efficacy of surface texture in providing depth cues to enhance depth perception across various media, including the real scene, virtual reality, and AR. However, these studies predominantly focus on black-and-white textures, leaving a gap in understanding the effectiveness of colored textures. To address this gap and further explore texture-related factors in AR, a series of experiments were conducted to investigate the effects of different texture cues on depth perception using the perceptual matching method. Findings indicate that the absolute depth error increases with decreasing contrast under black-and-white texture. Moreover, textures with higher color contrast also contribute to enhanced accuracy of depth judgments in AR. However, no significant effect of texture density on depth perception was observed. The findings serve as a theoretical reference for texture design in AR, aiding in the optimization of virtual-real registration processes.

Digital dynamic modeling and topology optimized design of shell face mills

This paper presents digital modeling of shell face mills in milling cylinder heads. The cutter's structural dynamics and its mode shapes are predicted using a Finite Element system. The geometries of the cutter body and inserts are imported from their Computer Aided Design (CAD) models. The insert edge is discretized into small segments to model its varying normal rake and inclination angles, which affect the cutting mechanics. The cutter is dynamically assembled with the target machine tool spindle using the receptance coupling method. A general dynamic cutting force model, which considers the varying edge geometry and inserts’ run-outs, is developed and used to predict cutting forces and chatter stability diagrams. The proposed model is experimentally verified to demonstrate the feasibility of the systematic application of physics-based digital design and analysis of tools for the mass machining of specific parts. The cutter body shape is optimized to increase the stiffness of the bending mode shape that caused chatter via topology optimization, which led to five-fold increase in the absolute stable depth of cut.

Factors Affecting Stimulus Duration Threshold for Depth Discrimination of Asynchronous Targets in the Intermediate Distance Range.

Binocular depth discrimination in the near distance range (< 2 m) improves with stimulus duration. However, whether the same response-pattern holds in the intermediate distance range (approximately 2-25 m) remains unknown because the spatial coding mechanisms are thought to be different. We used the two-interval forced choice procedure to measure absolute depth discrimination of paired asynchronous targets (3, 6, or 16 arc min). The paired targets (0.2 degrees) were located over a distance and height range, respectively, of 4.5 to 7.0m and 0.15 to 0.7 m. Experiment 1 estimated duration thresholds for binocular depth discrimination at varying target durations (40-1610 ms), in the presence of a 2 × 6 array of parallel texture-elements spanning 1.5 × 5.83 m on the floor. The texture-elements provided a visible background in the light-tight room (9 × 3 m). Experiment 2 used a similar setup to control for viewing conditions: binocular versus monocular and with versus without texture background. Experiment 3 compared binocular depth discrimination between brief (40, 80, and 125 ms) and continuous texture background presentation. Stimulus duration threshold for depth discrimination decreased with increasing disparity in experiment 1. Experiment 2 revealed depth discrimination performance with texture background was near chance level with monocular viewing. Performance with binocular viewing degraded without texture background. Experiment 3 showed continuous texture background presentation enhances binocular depth discrimination. Absolute depth discrimination improves with target duration, binocular viewing, and texture background. Performance further improved with longer background duration underscoring the role of ground surface representation in spatial coding.

Discriminating absolute depth of consecutively presented targets in the real environment with binocular disparity and relative height information

Discriminating absolute depth of consecutively presented targets in the real environment with binocular disparity and relative height information

Human walking biomechanics on sand substrates of varying foot sinking depth.

Our current understanding of human gait is mostly based on studies using hard, level surfaces in a laboratory environment. However, humans navigate a wide range of different substrates every day, which incur varied demands on stability and efficiency. Several studies have shown that when walking on natural compliant substrates there is an increase in energy expenditure. However, these studies report variable changes to other aspects of gait such as muscle activity. Discrepancies between studies exist even within substrate types (e.g. sand), which suggests that relatively 'fine-scale' differences in substrate properties exert quantifiable influences on gait mechanics. In this study, we compare human walking mechanics on a range of sand substrates that vary in overall foot sinking depth. We demonstrate that variation in the overall sinking depth in sand is associated with statistically significant changes in joint angles and spatiotemporal variables in human walking but exerts relatively little influence on pendular energy recovery and muscle activations. Significant correlated changes between gait metrics are frequently recovered, suggesting a degree of coupled or mechanistic interaction in their variation within and across substrates. However, only walking speed (and its associated spatiotemporal variables) correlate frequently with absolute foot sinkage depth within individual sand substrates, but not across them. This suggests a causative relationship between walking speed and foot sinkage depth within individual sand substates is not coupled with systematic changes in joint kinematics and muscle activity in the same way as is observed across sand substrates.

CRENet: Crowd region enhancement network for multi-person 3D pose estimation

Recovering multi-person 3D poses from a single image is a challenging problem due to inherent depth ambiguities, including root-relative depth and absolute root depth. Current bottom-up methods show promising potential to mitigate absolute root depth ambiguity through explicitly aggregating global contextual cues. However, these methods treat the entire image region equally during root depth regression, ignoring the negative impact of irrelevant regions. Moreover, they learn shared features for both depths, each of which focuses on different information. This sharing mechanism may result in negative transfer, thus diminishing root depth prediction accuracy. To address these challenges, we present a novel bottom-up method, Crowd Region Enhancement Network (CRENet), incorporating a Feature Decoupling Module (FDM) and a Global Attention Module (GAM). FDM explicitly learns the discriminative feature for each depth through adaptively recalibrating its channel-wise responses and fusing multi-level features, which makes the model focus on each depth prediction separately and thus avoids the adverse effect of negative transfer. GAM highlights crowd regions while suppressing irrelevant regions using the attention mechanism and further refines the attention regions based on the confidence measure about the attention, which is beneficial to learn depth-related cues from informative crowd regions and facilitate root depth estimation. Comprehensive experiments on benchmarks MuPoTS-3D and CMU Panoptic demonstrate that our method outperforms the state-of-the-art bottom-up methods in absolute 3D pose estimation and is applicable to in-the-wild images, which also indicates that learning depth-specific features and suppressing the noise signals can significantly benefit multi-person absolute 3D pose estimation.

Vibration suppression of series elastic actuator used for robotic grinding based on reconstructed hybrid optimized input shaper

Vibration suppression of the series elastic actuator (SEA) used for robotic compliant grinding is significant for guaranteeing force control accuracy and stability, which is important for material removal accuracy and surface quality improvement. This paper establishes a multi-feedforward control system to achieve vibration suppression for the SEA. The hybrid optimized input shaper (HOIS), instruction reconstructor, and nonlinear friction compensation model are seamlessly integrated into the multi-feedforward control method. Combination optimization principles are employed to formulate the HOIS, ensuring selectable performances on vibration suppression, response speed, and model stability when addressing the residual vibration caused by elastic elements. Base on the inherent delay characteristics of the shaper, a head/tail compression and intermediate shift instruction reconstruction (CSIR) method is introduced to address the tracking error induced by the time-delay of the shaper. Additionally, the friction compensator is established based on the Stribeck model and integrated into the feedforward control system to mitigate the vibration caused by the low-speed viscous friction. The effectiveness of the proposed method is verified by experimental tests. Results show that the HOIS has rich optional performance and can effectively suppress vibration. The tracking error caused by the shaper is effectively mitigated through the reconstruction of instructions. Grinding tests on curved parts are conducted on the robotic grinding system with SEA used as the force-control system. Compared with the benchmark method, the developed reconstructed hybrid optimized input shaper reduces the average force tracking error, the average absolute grinding depth error, and average roughness by 26.6 %, 22.5 %, and 21.5 % respectively. At the natural frequency of the grinding system, the amplitude of the frequency spectrum with the developed multi-feedforward method decreases by 48.13 %.

Optimizing spatial accuracy in electroencephalography reconstruction through diffuse optical tomography priors in the auditory cortex.

Diffuse optical tomography (DOT) enhances the localization accuracy of neural activity measured with electroencephalography (EEG) while preserving EEG's high temporal resolution. However, the spatial resolution of reconstructed activity diminishes for deeper neural sources. In this study, we analyzed DOT-enhanced EEG localization of neural sources modeled at depths ranging from 11-25 mm in simulations. Our findings reveal systematic biases in reconstructed depth related to DOT channel length. To address this, we developed a data-informed method for selecting DOT channels to improve the spatial accuracy of DOT-enhanced EEG reconstruction. Using our method, the average absolute reconstruction depth errors of DOT reconstruction across all depths are 0.9 ± 0.6 mm, 1.2 ± 0.9 mm, and 1.2 ± 1.1 mm under noiseless, low-level noise, and high-level noise conditions, respectively. In comparison, using fixed channel lengths resulted in errors of 2.6 ± 1.5 mm, 5.0 ± 2.6 mm, and 7.3 ± 4.5 mm under the same conditions. Consequently, our method improved the depth accuracy of DOT reconstructions and facilitated the use of more accurate spatial priors for EEG reconstructions, enhancing the overall precision of the technique.

Monocular Absolute Depth Estimation from Motion for Small Unmanned Aerial Vehicles by Geometry-Based Scale Recovery.

In recent years, there has been extensive research and application of unsupervised monocular depth estimation methods for intelligent vehicles. However, a major limitation of most existing approaches is their inability to predict absolute depth values in physical units, as they generally suffer from the scale problem. Furthermore, most research efforts have focused on ground vehicles, neglecting the potential application of these methods to unmanned aerial vehicles (UAVs). To address these gaps, this paper proposes a novel absolute depth estimation method specifically designed for flight scenes using a monocular vision sensor, in which a geometry-based scale recovery algorithm serves as a post-processing stage of relative depth estimation results with scale consistency. By exploiting the feature correspondence between successive images and using the pose data provided by equipped navigation sensors, the scale factor between relative and absolute scales is calculated according to a multi-view geometry model, and then absolute depth maps are generated by pixel-wise multiplication of relative depth maps with the scale factor. As a result, the unsupervised monocular depth estimation technology is extended from relative depth estimation in semi-structured scenes to absolute depth estimation in unstructured scenes. Experiments on the publicly available Mid-Air dataset and customized data demonstrate the effectiveness of our method in different cases and settings, as well as its robustness to navigation sensor noise. The proposed method only requires UAVs to be equipped with monocular camera and common navigation sensors, and the obtained absolute depth information can be directly used for downstream tasks, which is significant for this kind of vehicle that has rarely been explored in previous depth estimation studies.

Optimization Analysis of the Arrangement of the Submerged Floating Tunnel Subjected to Waves

The motion responses, mooring tensions, and submergence depth are the dominant factors for the arrangement of the Submerged Floating Tunnel (SFT) subjected to waves. Generally, the maximum values of motion responses, mooring tensions, and absolute submergence depth are mainly focused on. In the present study, experiments are implemented to measure the motion responses and mooring tensions of the SFT with different mooring patterns and submergence depths under waves with different characteristic wave heights and periods. In order to evaluate the arrangement of the SFT more effectively and comprehensively, besides the maximum values, several new characteristic parameters are introduced. Such parameters account for the motion responses in the frequency domain, the uniformity of the tension distribution, the length of time during which the cable reaches a relaxed condition during wave action, the KC number, the dimensionless period, the wave height, and the submergence depth. The results from the optimization analysis show the following: according to the characteristic values of motion responses and mooring tensions, the pattern of diagonal cables is better than that of diagonal cables + vertical cables; and within the range of the present experiments, there are optimal dimensionless parameters—the dimensionless submergence depth d0/LP ≥ 0.15, the KC number ≤ 0.8, or the dimensionless wave height Hs/d0 ≤ 0.10—for the condition of which the dynamic responses and mooring tensions vary slightly.

Investigating Pitting Behavior of Stainless-Steel for Canister in Vertical Position Using Electrochemical Acceleration Test

Stainless steel is renowned for its corrosion resistance and is widely used in constructing canisters for nuclear waste containment. These canisters are designed to endure burial in soil for over a century, making it crucial to study the long-term corrosion behavior of SS in this specific context. However, analysis of the corrosion behavior of SS is challenging due to its slow corrosion rate. Therefore, electrochemical acceleration methods are essential in studying the corrosion behavior of SS. This research employs the potentiostatic polarization test to comprehensively analyze SS pitting corrosion. The study focuses on vertically-positioned SS specimens, simulating canister conditions. Advanced microscopy techniques and simulation aid in understanding anolyte, pit depth, and pit shape behavior. Through this, the pitting propagation process of SS could be divided into four stages. Key outcomes of this study include models for absolute depth during propagation and the formation of secondary pitting. Comparative analysis with immersion tests reveals insights crucial for industrial installations safety and longevity.

A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation.

This study addresses the ongoing challenge for learning-based methods to achieve accurate object detection in foggy conditions. In response to the scarcity of foggy traffic image datasets, we propose a foggy weather simulation algorithm based on monocular depth estimation. The algorithm involves a multi-step process: a self-supervised monocular depth estimation network generates a relative depth map and then applies dense geometric constraints for scale recovery to derive an absolute depth map. Subsequently, the visibility of the simulated image is defined to generate a transmittance map. The dark channel map is then used to distinguish sky regions and estimate atmospheric light values. Finally, the atmospheric scattering model is used to generate fog simulation images under specified visibility conditions. Experimental results show that more than 90% of fog images have AuthESI values of less than 2, which indicates that their non-structural similarity (NSS) characteristics are very close to those of natural fog. The proposed fog simulation method is able to convert clear images in natural environments, providing a solution to the problem of lack of foggy image datasets and incomplete visibility data.

Lightweight monocular absolute depth estimation based on attention mechanism

Lightweight monocular absolute depth estimation based on attention mechanism

Lifelong-MonoDepth: Lifelong Learning for Multidomain Monocular Metric Depth Estimation.

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning (LL) models capable of estimating metric (absolute) depth. LL approaches potentially offer significant cost savings in terms of model training, data storage, and collection. However, the quality of RGB images and depth maps is sensor-dependent, and depth maps in the real world exhibit domain-specific characteristics, leading to variations in depth ranges. These challenges limit existing methods to LL scenarios with small domain gaps and relative depth map estimation. To facilitate lifelong metric depth learning, we identify three crucial technical challenges that require attention: 1) developing a model capable of addressing the depth scale variation through scale-aware depth learning; 2) devising an effective learning strategy to handle significant domain gaps; and 3) creating an automated solution for domain-aware depth inference in practical applications. Based on the aforementioned considerations, in this article, we present 1) a lightweight multihead framework that effectively tackles the depth scale imbalance; 2) an uncertainty-aware LL solution that adeptly handles significant domain gaps; and 3) an online domain-specific predictor selection method for real-time inference. Through extensive numerical studies, we show that the proposed method can achieve good efficiency, stability, and plasticity, leading the benchmarks by 8%-15%. The code is available at https://github.com/FreeformRobotics/Lifelong-MonoDepth.

Scale-preserving shape reconstruction from monocular endoscope image sequences by supervised depth learning.

Reconstructing 3D shapes from images are becoming popular, but such methods usually estimate relative depth maps with ambiguous scales. A method for reconstructing a scale-preserving 3D shape from monocular endoscope image sequences through training an absolute depth prediction network is proposed. First, a dataset of synchronized sequences of RGB images and depth maps is created using an endoscope simulator. Then, a supervised depth prediction network is trained that estimates a depth map from a RGB image minimizing the loss compared to the ground-truth depth map. The predicted depth map sequence is aligned to reconstruct a 3D shape. Finally, the proposed method is applied to a real endoscope imagesequence.

A Geometric Knowledge Oriented Single-Frame 2D-to-3D Human Absolute Pose Estimation Method

As a critical part of the 3D human pose estimation (HPE), establishing the 2D-to-3D lifting mapping is limited by depth ambiguity. Most current works generally lack the quantitative analysis of the relative depth expression and the depth ambiguity error expression in lifting mapping, resulting in low prediction efficiency and poor interpretability. To this end, this paper mines and leverages prior geometric knowledge of these expressions based on the pinhole imaging principle, decoupling the 2D-to-3D lifting mapping and simplifying the model training. Specifically, this paper proposes a prior geometric knowledge oriented pose estimation model with two-branch transformer architectures, explicitly introducing high-dimensional prior geometric features to improve model efficiency and interpretability. It converts the regression of spatial coordinates into the prediction of spatial direction vectors between joints to generate multiple feasible solutions further alleviate the depth ambiguity. Moreover, this paper raises a novel non-learning-based absolute depth estimation algorithm based on prior geometric relationship decoupling from relative depth expression for the first time. It establishes multiple independent depth mapping from non-root nodes to the root node to calculate the absolute depth candidate, which is parameter-free, plug-and-play, and interpretable. Experiments show that the proposed pose estimation model achieves state-of-the-art performance on Human 3.6M and MPI-INF-3DHP benchmarks with lower parameters and faster inference speed, and the proposed absolute depth estimation algorithm achieves similar performance to traditional methods without any network parameters. The source code are available at https://github.com/Humengxian/GKONet.

High-resolution aerosol data from the top 3.8 kyr of the East Greenland Ice coring Project (EGRIP) ice core

Abstract. Here we present the high-resolution continuous flow analysis (CFA) data from the top 479 m of the East Greenland Ice coring Project (EGRIP) ice core covering the past 3.8 kyr. The data consist of 1 mm depth-resolution profiles of calcium, sodium, ammonium, nitrate, and electrolytic conductivity as well as decadal averages of these profiles. The nominally 1 mm data represent an oversampling of the record as the true resolution is limited by the analytical setup to approximately 1 cm. Alongside the data we provide a description of the measurement setup, procedures, the relevant references for the specific methods as well as an assessment of the precision of the measurements, the sample-to-depth assignment, and the depth and temporal resolution of the data set. The error in absolute depth assignment of the data may be on the order of 2 cm; however, relative depth offsets between the records of the individual species are only on the order of 1 mm. The presented data have sub-annual resolution over the entire depth range and have already formed part of the data for an annually layer-counted timescale for the EGRIP ice core used to improve and revise the multi-core Greenland ice-core chronology (GICC05) to a new version, GICC21 (Sinnl et al., 2022). The data are available in full 1 mm resolution and decadal averages on PANGAEA (https://doi.org/10.1594/PANGAEA.945293, Erhardt et al., 2022b).

Multi-view stitching phase measuring deflectometry for freeform specular surface metrology.

Phase measuring deflectometry (PMD) offers notable advantages for precision inspection of specular elements. Nevertheless, if confronts challenges when measuring freeform specular surfaces due to the dispersion of reflection rays from surfaces with high local slopes. Here, we propose a multi-view stitching PMD. It utilizes distinct sensors combining with a screen to capture the appearance of each region. After precisely calibrating the entire system to correct the absolute depth of each region, the appearances of all regions are precisely stitched together, reconstructing the comprehensive appearance of the surface. Through experimental setup, we measured the 3D morphology of a spherical lens with a curvature radius of 155.04 mm and a peak-to-valley (PV) value of 2.9 mm, which yielded a measurement accuracy of 5.3 µm (relative error: 0.18 %). Furthermore, we successfully measured the appearance of a curved mobile phone screen with local slopes ranging from -46.1° to 51.3°, and freeform acrylic sheet with local slopes ranging from -6.7° to 7.7° and a PV value of 5.3 mm.