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

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Published: 2026-03-31
Keywords:
depth estimation, performance benchmarking, GPU acceleration, unstructured environments

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Yuhan Li
Computer Science, Northeastern University, Boston, MA, USA

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.

Article Details

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Vol. 2 No. 2 (2026)

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Articles

How to Cite

Performance Benchmarking and Optimization Strategies for Depth Estimation Algorithms in Unstructured Environments. (2026). Journal of Sustainability, Policy, and Practice, 2(2), 32-43. https://schoalrx.com/index.php/jspp/article/view/96

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