# The Relative Importance of Depth Cues and Semantic Edges for Indoor Mobility Using Simulated Prosthetic Vision in Immersive Virtual Reality

arXiv: https://arxiv.org/abs/2208.05066 · HTML: https://mlsanity.com/paper/2208.05066/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2022.

## Built on by (most cited first, 10 of 10)

- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Depth Anything with Any Prior](https://mlsanity.com/paper/2505.10565/) (2025)
- [DepthLab: From Partial to Complete](https://mlsanity.com/paper/2412.18153/) (2024)
- [QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge](https://mlsanity.com/paper/2503.16709/) (2025)
- [Improved monocular depth prediction using distance transform over pre-semantic contours with self-supervised neural networks](https://mlsanity.com/paper/2605.08320/) (2026)
- [Predicting High-precision Depth on Low-Precision Devices Using 2D Hilbert Curves](https://mlsanity.com/paper/2405.14024/) (2024)
- [VistaDepth: Improving far-range Depth Estimation with Spectral Modulation and Adaptive Reweighting](https://mlsanity.com/paper/2504.15095/) (2025)
- [DepthDark: Robust Monocular Depth Estimation for Low-Light Environments](https://mlsanity.com/paper/2507.18243/) (2025)
- [BRIDGE -- Building Reinforcement-Learning Depth-to-Image Data Generation Engine for Monocular Depth Estimation](https://mlsanity.com/paper/2509.25077/) (2025)
- [ReDepth Anything: Test-Time Depth Refinement via Self-Supervised Re-lighting](https://mlsanity.com/paper/2512.17908/) (2025)

## Abstract (arXiv)

Visual neuroprostheses (bionic eyes) have the potential to treat degenerative eye diseases that often result in low vision or complete blindness. These devices rely on an external camera to capture the visual scene, which is then translated frame-by-frame into an electrical stimulation pattern that is sent to the implant in the eye. To highlight more meaningful information in the scene, recent studies have tested the effectiveness of deep-learning based computer vision techniques, such as depth estimation to highlight nearby obstacles (DepthOnly mode) and semantic edge detection to outline important objects in the scene (EdgesOnly mode). However, nobody has attempted to combine the two, either by presenting them together (EdgesAndDepth) or by giving the user the ability to flexibly switch between them (EdgesOrDepth). Here, we used a neurobiologically inspired model of simulated prosthetic vision (SPV) in an immersive virtual reality (VR) environment to test the relative importance of semantic edges and relative depth cues to support the ability to avoid obstacles and identify objects. We found that participants were significantly better at avoiding obstacles using depth-based cues as opposed to relying on edge information alone, and that roughly half the participants preferred the flexibility to switch between modes (EdgesOrDepth). This study highlights the relative importance of depth cues for SPV mobility and is an important first step towards a visual neuroprosthesis that uses computer vision to improve a user's scene understanding.