Depth Potentiality Perception

• Depth Potentiality Perception is defined as the quantification and modulation of depth cues across biological and computational systems, enhancing tasks such as object segmentation and localization.
• It integrates psychophysical thresholds with neural network architectures, leveraging early fusion of RGB and depth data to improve accuracy in control, saliency, and obstacle avoidance.
• Depth potentiality is formally measured using information-theoretic bounds and optimized depth plane allocations, which are critical for designing robust display systems and improving amodal completion performance.

Depth Potentiality Perception

Depth potentiality perception refers to the quantification, exploitation, or modulation of the capability—in biological or artificial systems—of depth cues or measurements to enhance perceptual organization, scene understanding, and downstream tasks such as control, localization, or object segmentation. This construct appears across experimental psychophysics, computational perception, robot control, RGB-D vision, neural attention mechanisms, light-field display optimization, and amodal completion. Its rigorous treatment involves both formal metrics (e.g., coverage, information-theoretic bounds, statistical learning models) and psychophysical thresholds, as well as network architectures designed to maximize performance subject to the quality and reliability of depth input.

1. Theoretical Foundations and Definitions

Depth potentiality perception quantifies the intrinsic or context-dependent capacity of depth information to contribute meaningfully to perception or action:

• In computational and robotic domains, depth potentiality is the untapped or modulated ability of depth measurements—relative to RGB or 2D cues—to improve primary tasks (e.g., control, saliency, obstacle avoidance) (Clement et al., 20 Mar 2025, Chen et al., 2020).
• In psychophysical and neurocognitive contexts, depth potentiality arises from the fusion of monocular (e.g., shading, color-surround polarity) and binocular (e.g., disparity) cues, with evidence that subjective percepts of size and depth are statistically and mechanistically linked (Dresp-Langley et al., 2019).
• Formally, in display and vision system design, depth potentiality can be the maximal number and placement of quantized depth planes required to saturate the perceptual discrimination ability of the eye (monocular and stereoscopic components) (Aghasi et al., 2020).

A notable formalization is the “depth potentiality label” $D(\widetilde I, G)$ in RGB-D saliency detection, measuring overlap between thresholded depth and ground-truth saliency via F-measure style metrics (Chen et al., 2020).

2. Psychophysical and Neural Mechanisms

Psychophysical studies delineate the cues and limits underlying depth potentiality:

• Depth discrimination arises not solely from geometric disparity but also from relative size, color-surround polarity, and global scene statistics. These cues interact in large-scale recurrent neural architectures—such as FACADE/LAMINART—where both edge- and surface-based signals propagate to determine border ownership and depth ordering (Dresp-Langley et al., 2019).
• Subjective depth and size judgments co-vary almost perfectly ($r>0.98$). For human vision, color and luminance combinations (not contrast alone) dominate apparent depth assignment. The macro-context, such as global background luminance, exerts systematic effects on perceived depth, highlighting the distributed inference across multiple neural layers and feedback loops.
• In autostereograms, the spectral content of the stimulus dictates the ease of “locking in” to the depth: 1/f (pink) noise provides scale-invariant matching basin sharpness, optimal for rapid and fine-grained depth recovery, while white or brown noise degrade performance at either coarse or fine scales (Yankelevsky et al., 2015).

3. Computational Models and RGB-D Network Architectures

Depth potentiality shapes architecture design at both the feature extraction and cross-modal fusion stages:

• In RGB-D control tasks, the additive value of depth manifests most strongly when fusion occurs early (by concatenating depth and RGB channels before spatial encoding). Early fusion enables the learning of robust cross-modal features that propagate through recurrent controllers (LSTM, LTC, CfC, LRC), resulting in lower mean-square error and greater resilience to noise and frame drops. Late fusion and depth-adapted convolutions (DCN, ZACN) provide marginal or scenario-dependent gains (Clement et al., 20 Mar 2025).
• In attention-guided salient object detection (DPANet), depth potentiality is learned end-to-end as a confidence scalar $g \in [0,1]$. The estimated $g$ value, extracted from global pooled features, gates the bi-directional cross-modal attention modules (GMA), ensuring dynamic suppression of noisy depth and enhancement when the depth channel is reliable. Networks equipped with depth potentiality perception demonstrably reduce mean absolute error and maximize F-measure on benchmark datasets (Chen et al., 2020).
• In amodal perception, networks combine observed (modal) depth with segmentation-derived amodal masks. Guidance channels and learned priors allow generation or regression of consistent depth estimates for both visible and occluded regions, demonstrating significant improvement in relative depth error and accuracy (Li et al., 3 Dec 2024).

4. Quantification and Optimality in Human and Display Systems

The quantification of depth potentiality in human perception and simulated systems is addressed via optimization and statistical modeling:

• For light field and stereoscopic display design, depth potentiality is the smallest set of quantized depth planes such that the residual “uncovered” depth-blur space lies below the threshold of optical or neural discrimination. For monocular accommodation this number is ≈8; for stereoscopic cues, ≈1731 discrete planes suffice across 0.25 m to infinity (Aghasi et al., 2020).
• The optimization framework uses maximal hypograph coverage with convex relaxation on allocation variables, yielding provably optimal plane placements. The first three optimal monocular depth planes are at ≈53, 90, and 170 cm.
• In autostereogram design, optimizing the spectral slope of the base pattern for 1/f noise ensures depth cues at all scales are maximally salient for the coarse-to-fine stereo correspondence process, thereby realizing the highest depth potentiality (Yankelevsky et al., 2015).

Modality/System	Depth Levels to Saturate Perception	Optimization Criterion
Monocular accommodation	≈ 8	Eye’s depth-of-field blur threshold
Stereoscopic discrimination	≈ 1731	Human stereo-acuity angular threshold
RGB-D neural attention	Per-image scalar $g \in [0,1]$	Overlap with task-driven saliency function

5. Practical Applications and Technological Implementations

Depth potentiality perception underpins advances in several application areas:

• Autonomous agents: Robust policy learning and real-time control in indoor navigation and racing, with early-fusion RGB-D architectures yielding 20–40% MSE reductions over RGB-only models and maintaining 100% success in adverse conditions (Clement et al., 20 Mar 2025).
• Salient object detection: Cross-modal gated attention, dynamically modulated by learned depth potentiality, prevents performance collapse when depth input is noisy or misaligned, and preserves gains when it is reliable (Chen et al., 2020).
• Obstacle avoidance in robotics: Probabilistic depth perception via particle filters, combined with artificial potential functions, creates smooth avoidance vector fields robust to noise, missing observations, and low frame rates (Ahmad et al., 2020).
• Amodal completion: Relative depth estimation methods, informed by composited masks and depth priors, reconstruct plausible geometry in the occluded image regions for tasks such as inpainting, scene understanding, and semantic reasoning (Li et al., 3 Dec 2024).
• Spatial AR guidance: In OST-AR, the strength of occlusion cues (opaque virtual targets and real-tool masking) directly correlates with perceived depth potentiality, improving localization error, usability, and reducing workload. Lapses in tracking or high transparency modulate this potential and should be managed contextually (Yang et al., 25 Aug 2025).
• Display and visualization design: Optimizing quantized depth planes in band-limited light-field architectures and autostereogram patterns maximizes perceived spatial continuity and minimizes resource footprint (Aghasi et al., 2020, Yankelevsky et al., 2015).

6. Limitations, Contextual Dependencies, and Future Directions

Depth potentiality is inherently context- and modality-dependent:

• Sensor limitations (e.g., depth sensor range, noise floor, and spatial resolution) restrict practical exploitation in real-world navigation and control (Clement et al., 20 Mar 2025).
• Learned depth potentiality gates in neural networks depend critically on the availability and alignment of high-quality task supervision; hallucinations or failure cases can arise from mask ambiguity or texture loss (Chen et al., 2020, Li et al., 3 Dec 2024).
• Human depth discrimination is modulated by macro-context cues (scene luminance, color polarity), so optimal coding and display must account for both local and global scene statistics (Dresp-Langley et al., 2019).
• In AR systems, the absence of robust tool tracking or occlusion handling reduces depth potentiality and user performance; adaptive rendering and real-time tracking are recommended to maintain high-fidelity perception (Yang et al., 25 Aug 2025).
• Foreseeable advances include integrating depth uncertainty models, joint estimation of amodal segmentation and shape, temporal context exploitation, and leveraging deep-learning-based deblurring to extend the operational regimes of computational and perceptual systems (Li et al., 3 Dec 2024, Kerschner et al., 2023).

7. Synthesis and Cross-Domain Relevance

Depth potentiality perception offers a rigorous, multi-scale framework unifying biological, computational, and engineering approaches to depth inference. Its operationalization spans psychophysical measurement, information-theoretic display optimization, neural network attention gating, probabilistic estimation, and application-specific architectural choices. The convergence of these lines of research substantiates depth potentiality perception as a keystone for robust, scalable 3D perception systems in both biological and artificial agents. Continued cross-pollination between cognitive science and deep learning is likely to refine both mechanistic understanding and system-level exploitation of depth cues across domains.

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References:

• (Clement et al., 20 Mar 2025) Depth Matters: Multimodal RGB-D Perception for Robust Autonomous Agents
• (Yang et al., 25 Aug 2025) Impact of Target and Tool Visualization on Depth Perception and Usability in Optical See-Through AR
• (Dresp-Langley et al., 2019) Correlated Effects of Relative Size and Depth in the Perceptual Organization of Multiple Figure-Ground Configurations
• (Ahmad et al., 2020) APF-PF: Probabilistic Depth Perception for 3D Reactive Obstacle Avoidance
• (Chen et al., 2020) DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection
• (Kerschner et al., 2023) Stereoscopic Depth Perception Through Foliage
• (Yankelevsky et al., 2015) Depth Perception in Autostereograms: 1/f-Noise is Best
• (Aghasi et al., 2020) Optimal Allocation of Quantized Human Eye Depth Perception for Light Field Display Design
• (Li et al., 3 Dec 2024) Amodal Depth Anything: Amodal Depth Estimation in the Wild