The article presents 'Panopticus', an innovative system for omnidirectional 3D object detection that employs a multi-branch model with spatial-adaptive execution. It dynamically schedules inference configurations in real-time, aiming to maximize accuracy while maintaining specified latency targets. Performance predictions guide the optimal allocation of image-branch pairs, taking into account various spatial characteristics that affect detection. This adaptive scheduling not only improves detection capabilities but also offers robustness in fluctuating environments, making Panopticus suitable for diverse applications in 3D object detection.
The proposed Panopticus system employs dynamic scheduling of its multi-branch model to optimize object detection accuracy while adhering to latency constraints, enhancing overall performance.
By utilizing performance predictions for various image-branch pairs, the scheduler effectively manages to allocate branches to images, enabling a methodical approach to maximized detection strategies.
The focus on spatial characteristics during the performance prediction phase allows Panopticus to adapt dynamically to changing environments, providing robustness in 3D object detection tasks.
The overarching goal of the model adaptation strategy in Panopticus is to achieve efficient real-time object detection in diverse scenarios through smart scheduling and execution.
#3d-object-detection #dynamic-scheduling #performance-prediction #model-adaptation #spatial-awareness
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