Efficient high-dynamic-range depth map processing with reduced precision neural net accelerator

Abstract

Depth sensing technology has become important in a number of consumer, robotics, and automated driving applications. However, the depth maps generated by such technologies today still suffer from limited resolution, sparse measurements, and noise, and require significant post-processing. Depth map data often has higher dynamic range than common 8-bit image data and may be represented as 16-bit values. Deep convolutional neural nets can be used to perform denoising, interpolation and completion of depth maps; however, in practical applications there is a need to enable efficient low-power inference with 8-bit precision. In this paper, we explore methods to process high-dynamic-range depth data using neural net inference engines with 8-bit precision. We propose a simple technique that attempts to retain signal-to-noise ratio in the post-processed data as much as possible and can be applied in combination with most convolutional network models. Our initial results using depth data from a consumer camera device show promise, achieving inference results with 8-bit precision that have similar quality to floating-point processing.