The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement

• URL: http://arxiv.org/abs/2111.13738v1
• Date: Fri, 26 Nov 2021 20:24:07 GMT
• Title: The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement
• Authors: Ilya Chugunov, Yuxuan Zhang, Zhihao Xia, Cecilia Zhang, Jiawen Chen, and Felix Heide
• Abstract summary: We show how we can combine dense micro-baseline parallax cues with kilopixel LiDAR depth estimates during viewfinding. The proposed method brings high-resolution depth estimates to 'point-and-shoot' tabletop photography and requires no additional hardware, artificial hand motion, or user interaction beyond the press of a button.
• Score: 25.637162990928676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern smartphones can continuously stream multi-megapixel RGB images at 60~Hz, synchronized with high-quality 3D pose information and low-resolution LiDAR-driven depth estimates. During a snapshot photograph, the natural unsteadiness of the photographer's hands offers millimeter-scale variation in camera pose, which we can capture along with RGB and depth in a circular buffer. In this work we explore how, from a bundle of these measurements acquired during viewfinding, we can combine dense micro-baseline parallax cues with kilopixel LiDAR depth to distill a high-fidelity depth map. We take a test-time optimization approach and train a coordinate MLP to output photometrically and geometrically consistent depth estimates at the continuous coordinates along the path traced by the photographer's natural hand shake. The proposed method brings high-resolution depth estimates to 'point-and-shoot' tabletop photography and requires no additional hardware, artificial hand motion, or user interaction beyond the press of a button.

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