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arxiv_cv 95% Match Research Paper Robotics Engineers,Computer Vision Researchers,3D Graphics Developers,Hardware Engineers 2 weeks ago

LookUp3D: Data-Driven 3D Scanning

computer-vision › 3d-vision
📄 Abstract

Abstract: High speed, high-resolution, and accurate 3D scanning would open doors to many new applications in graphics, robotics, science, and medicine by enabling the accurate scanning of deformable objects during interactions. Past attempts to use structured light, time-of-flight, and stereo in high-speed settings have usually required tradeoffs in resolution or inaccuracy. In this paper, we introduce a method that enables, for the first time, 3D scanning at 450 frames per second at 1~Megapixel, or 1,450 frames per second at 0.4~Megapixel in an environment with controlled lighting. The key idea is to use a per-pixel lookup table that maps colors to depths, which is built using a linear stage. Imperfections, such as lens-distortion and sensor defects are baked into the calibration. We describe our method and test it on a novel hardware prototype. We compare the system with both ground-truth geometry as well as commercially available dynamic sensors like the Microsoft Kinect and Intel Realsense. Our results show the system acquiring geometry of objects undergoing high-speed deformations and oscillations and demonstrate the ability to recover physical properties from the reconstructions.
Authors (6)
Giancarlo Pereira
Yidan Gao
Yurii Piadyk
David Fouhey
Claudio T Silva
Daniele Panozzo
Submitted
April 5, 2024
arXiv Category
cs.CV
arXiv PDF

Key Contributions

This paper introduces a novel method for 3D scanning that achieves unprecedented speed (450 fps at 1MP, 1450 fps at 0.4MP) by using a per-pixel lookup table mapping colors to depths. This approach enables accurate scanning of deformable objects during interactions, overcoming the speed-resolution trade-offs of previous technologies and demonstrating a functional hardware prototype.

Business Value

Opens up new possibilities for real-time 3D interaction and analysis in fields like robotics, VR/AR, and medical imaging, potentially creating new markets for high-fidelity dynamic 3D capture.