End-to-End Latency Optimization of Multi-view 3D Reconstruction for Disaster Response

• URL: http://arxiv.org/abs/2304.01488v1
• Date: Tue, 4 Apr 2023 03:04:44 GMT
• Title: End-to-End Latency Optimization of Multi-view 3D Reconstruction for Disaster Response
• Authors: Xiaojie Zhang, Mingjun Li, Andrew Hilton, Amitangshu Pal, Soumyabrata Dey, Saptarshi Debroy
• Abstract summary: Multi-view Stereo (MVS) based 3D reconstruction applications are exceedingly time consuming, especially when run on such computationally constrained mobile edge devices. In this paper, we aim to design a latency optimized MVS algorithm pipeline, with the objective to best balance the end-to-end latency and reconstruction quality.
• Score: 3.471012855429593
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In order to plan rapid response during disasters, first responder agencies often adopt `bring your own device' (BYOD) model with inexpensive mobile edge devices (e.g., drones, robots, tablets) for complex video analytics applications, e.g., 3D reconstruction of a disaster scene. Unlike simpler video applications, widely used Multi-view Stereo (MVS) based 3D reconstruction applications (e.g., openMVG/openMVS) are exceedingly time consuming, especially when run on such computationally constrained mobile edge devices. Additionally, reducing the reconstruction latency of such inherently sequential algorithms is challenging as unintelligent, application-agnostic strategies can drastically degrade the reconstruction (i.e., application outcome) quality making them useless. In this paper, we aim to design a latency optimized MVS algorithm pipeline, with the objective to best balance the end-to-end latency and reconstruction quality by running the pipeline on a collaborative mobile edge environment. The overall optimization approach is two-pronged where: (a) application optimizations introduce data-level parallelism by splitting the pipeline into high frequency and low frequency reconstruction components and (b) system optimizations incorporate task-level parallelism to the pipelines by running them opportunistically on available resources with online quality control in order to balance both latency and quality. Our evaluation on a hardware testbed using publicly available datasets shows upto ~54% reduction in latency with negligible loss (~4-7%) in reconstruction quality.

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