Performance Benchmarking and Optimization Strategies for Depth Estimation Algorithms in Unstructured Environments

Yuhan Li

Journal of Sustainability, Policy, and Practice, 2026, vol. 2, issue 2, 32-43

Abstract: The deployment of depth estimation algorithms in autonomous robotic systems necessitates comprehensive performance evaluation beyond traditional accuracy metrics. This research establishes a standardized benchmarking framework that quantifies multidimensional trade-offs among estimation accuracy, inference latency, and computational resource consumption across diverse hardware configurations. Through a systematic evaluation of representative algorithms on GPU-accelerated platforms, we identify critical bottlenecks that affect real-time performance and propose data-driven optimization strategies. Our experimental analysis shows that algorithm-hardware matching decisions significantly impact operational efficiency, with throughput varying by roughly 3-4× across the evaluated configurations. The proposed framework enables developers to make informed deployment decisions based on quantitative performance profiles tailored to specific application requirements.

Keywords: depth estimation; performance benchmarking; GPU acceleration; unstructured environments (search for similar items in EconPapers)
Date: 2026
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