The article discusses a multi-branch model for omnidirectional 3D object detection, focusing on modular design that accommodates varying device capabilities and latency targets. Each branch represents distinct execution paths for processing images through different detection models, enhancing detection flexibility. The model's architecture promotes resource efficiency by eliminating redundancy and adapting to contextual demands, optimizing depth prediction in terms of quality and latency. The implementation aims to maintain high performance across various environments while addressing challenges in 3D detection under diverse operational conditions.
The multi-branch design of the omnidirectional 3D detection model modularizes processing capabilities, allowing tailored execution paths for different regions, enhancing efficiency.
By utilizing a modular model design, we reduce redundancy, enabling adaptable depth predictions to meet diverse latency requirements without compromising quality.
Our approach modularizes detection capabilities, allowing optimized performance based on device specifications and operational constraints, while keeping inference adaptable to real-time needs.
The innovative design encapsulates core modules allowing each to execute based on contextual demands, ensuring potent balance between performance and resource utilization.
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