3D Neural Rendering for Automated Ultrasonic Laser Scans
3D Neural Rendering for Automated Ultrasonic Laser Scans
This research presents a method for reconstructing the steady-state surface wavefield of three-dimensional parts by integrating automated robotic manipulation with neural rendering.
The study demonstrates that combining a 6-DOF robotic manipulator with a modified Neural Radiance Field pipeline can reconstruct steady-state surface wavefields from laser doppler vibrometer scans, achieving a mean ZNCC of 0.60 on held-out views for defect detection in welded components.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2611.00527v1 Announce Type: cross Abstract: Laser doppler vibrometer (LDV) scans can be used to detect defects and cracks in manufactured parts before they are deployed. This work presents a method for reconstructing the steady-state surface wavefield of a three-dimensional part by combining automated robotic manipulation with neural rendering. A six-degree-of-freedom (6-DOF) robotic manipulator (UFactory xArm6
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) with an embedded piezoelectric transducer presented an acoustically excited test article to a stationary LDV at a sequence of known orientations, yielding a set of complex-valued 2D scans with poses derived analytically from the arm kinematics rather than from image-based pose estimation. These scans were used to train a novel complex-nerfacto model, a modified Neural Radiance Field (NeRF) pipeline that replaces th
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e standard three-channel RGB output head with a two-channel output. Complex-nerfacto represents the real and imaginary velocity components of a wavefield, preserving the complex measurement without quantization. Reconstructions of a welded aluminum cylinder and a welded T-bracket were evaluated against measured scans using Z-normalized cross-correlation (ZNCC). The complex-valued pipeline achieved a mean ZNCC of 0.60
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Source：arXiv Robotics — research abstracts · arxiv.org