On Balancing Latency and Quality of Edge-native Multi-view 3D Reconstruction
Xiaojie Zhang, Houchao Gan, Amitangshu Pal, Soumyabrata Dey, Saptarshi Debroy · 2023
Multi-view 3D reconstruction driven augmented, virtual, and mixed reality applications are becoming increasingly edge-native, due to factors such as, rapid reconstruction needs, security/privacy concerns, and lack of connectivity to cloud platforms. Managing edge-native 3D reconstruction, due to edge resource constraints and inherent dynamism of 'in the wild' 3D environments, involves striking a balance between conflicting objectives of achieving rapid reconstruction and satisfying minimum quality requirements. In this paper, we take a deeper dive into multi-view 3D reconstruction latency-quality trade-off, with an emphasis on reconstruction of dynamic 3D scenes. We propose data-level and task-level parallelization of 3D reconstruction pipelines, holistic edge system optimizations to reduce reconstruction latency, and long-term minimum reconstruction quality satisfaction. The proposed solutions are validated through collection of real-world 3D scenes with varying degree of dynamism that are used to perform experiments on hardware edge testbed. The results show that our solutions can achieve between 50% to 75% latency reduction without violating long term minimum quality requirements.