The article explores Panopticus, a novel approach to adaptive omnidirectional 3D object detection, currently designed for single-task environments. It emphasizes the need for future improvements focused on multi-task execution, critical for applications like mobile robotics. Key areas of development include monitoring runtime dynamics for resource contention, co-designing multi-branch models tailored for various tasks, and utilizing heterogeneous processors to optimize performance. The evaluation metrics employed, including detection score and mAP, are crucial in assessing the effectiveness and efficiency of the Panopticus model in real-world applications.
In the initial rollout of Panopticus, our focus on single-task execution shows performance consistency, yet future iterations need to address multi-task environments for real-world application.
To enhance performance, we must monitor the impact of resource contention in concurrent tasks while co-designing specialized models suited for different 3D detection applications.
Utilizing heterogeneous processors effectively, like CPUs and NPUs, holds the potential to significantly boost efficiency in handling multi-task workloads in 3D object detection.
The selection of performance metrics such as detection score and mAP is fundamental, as they provide critical insights into the thorough evaluation of our 3D detection system.
#3d-object-detection #omnidirectional-systems #panopticus #multi-task-execution #performance-evaluation
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