An active-learning framework for real-time depth perception from monocular vision streams

2026-08-05

AI Summary

This work addresses the vulnerability of lightweight monocular depth estimation models to domain shift in dynamic environments and their performance saturation due to static training paradigms. To overcome these limitations, the authors propose an online active learning framework featuring a closed-loop predict–evaluate–correct mechanism that actively selects high-informativeness samples from incoming visual streams for real-time model updating. By integrating selective plasticity with Elastic Weight Consolidation (EWC), the approach enables localized parameter adaptation while preserving globally learned knowledge, thereby breaking through the static optimization bottleneck inherent in compact architectures. Implemented on MobileNetV3-Small, the method achieves competitive accuracy with approximately 75% reduction in computational cost, demonstrating the critical role of controlled parameter plasticity in enabling effective dynamic adaptation.

Abstract

Biological visual systems can perceive depth from monocular vision flow, continuously integrating temporal visual cues while maintaining a balance between stability and plasticity in dynamic environments. In contrast, artificial perception models deployed on resource-constrained edge devices are typically trained in a static offline manner and remain frozen after deployment, often suffering severe performance degradation under domain shifts. While large-scale models may encode broad knowledge through massive parameter redundancy, lightweight networks face a static optimization dilemma: forcing compact models to learn universal geometric representations is computationally inefficient and often leads to performance saturation. To resolve this issue, an Online Active Learning (OAL) mechanism is introduced to endow compact neural networks with the capability to adapt continuously during operation. A closed-loop Predict-Evaluate-Correct learning paradigm is established to actively select high-confidence, information-rich signals from streaming visual input. Crucially, Elastic Weight Consolidation (EWC) is employed not merely to prevent catastrophic forgetting, but to enforce Selective Plasticity, preserving parameters that encode globally relevant structural knowledge while allowing local alignment to newly observed environments. Built upon a MobileNetV3-Small backbone, the proposed system achieves approximately a 75% reduction in computational cost while maintaining competitive depth estimation accuracy. Experimental results demonstrate that adaptability is not solely determined by model size, but rather by how effectively parameter plasticity is regulated in dynamic environments.

Problem

• monocular depth estimation
• domain shift
• edge devices
• model plasticity
• online adaptation

Innovation

• Online Active Learning
• Selective Plasticity
• Monocular Depth Estimation
• Elastic Weight Consolidation
• Edge Adaptation