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ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems

ECCV 2018 • Yinda Zhang • Sameh Khamis • Christoph Rhemann • Julien Valentin • Adarsh Kowdle • Vladimir Tankovich • Michael Schoenberg • Shahram Izadi • Thomas Funkhouser • Sean Fanello

In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces precise depth with a subpixel precision of $1/30th$ of a pixel; it does not suffer from the common over-smoothing issues; it preserves the edges; and it explicitly handles occlusions... (read more)

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