Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters

Hendrik Sommerhoff, Shashank Agnihotri, Mohamed Emam Saleh, Michael Moeller, Margret Keuper, Bhaskar Choubey, Andreas Kolb

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: sensors, computer vision, computer graphics, semantic segmentation, classification

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Abstract:

Computational imaging concepts based on integrated edge AI and and neural sensor concepts solve vision problems in an end-to-end, task-specific manner, by jointly optimizing the algorithmic and hardware parameters to sense data with high information value. They yield energy, data, and privacy efficient solutions, but rely on novel hardware concepts, yet to be scaled up. In this work, we present the first truly end-to-end trained imaging pipeline that optimizes imaging sensor parameters, available in standard CMOS design methods, jointly with the parameters of a given neural network on a specific task. Specifically, we derive an analytic, differentiable approach for the sensor layout parameterization that allows for task-specific, local varying pixel resolutions. We present two pixel layout parameterization functions: rectangular and curvilinear grid shapes that retain a regular topology. We provide a drop-in module that approximates sensor simulation given existing high-resolution images to directly connect our method with existing deep learning models. We show that network predictions benefit from learnable pixel layouts for two different downstream tasks, classification and semantic segmentation. Moreover, we give a fully featured design for the hardware implementation of the learned chip layout for a semantic segmentation task.

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Submission Number: 7246