ArXiv

FasTac: A Curved Multispectral Vision-Based Tactile Sensor for High-Speed High-Precision 3D Shape and Force Perception

2026-07-30 · 16:00 UTC ·Xiaofan Lu, Kaiji Huang, Jiahui Chen... ·1 min read
Authors
Xiaofan Lu, Kaiji Huang, Jiahui Chen...
Categories
cs.RO
arXiv
https://arxiv.org/abs/2607.28416v1
PDF
https://arxiv.org/pdf/2607.28416v1

Brief

FasTac presents a curved multispectral vision-based tactile fingertip combining single-image multispectral photometric stereo, boundary-prior fast Poisson depth reconstruction, and a position-aware dynamic-convolution estimator (HyperForce) for three-axis forces. Results report depth MAE reduction to 0.0415 mm, NMAE of 2.74% (normal) and 2.39% (shear), and FPGA runtime of 1.09 ms, validating high-precision, high-speed 3D and dynamic force sensing.

Why it matters

FasTac is a curved, multispectral vision-based tactile fingertip that uses single-image multispectral photometric stereo plus a boundary-prior fast Poisson solver to reconstruct 3D contact geometry; adding NIR illumination and the boundary prior reduced depth MAE from 0.2730 mm to 0.0415 mm.

Key details

  • HyperForce employs position-aware dynamic convolution to model spatially nonuniform elastomer response and estimate three-axis forces, achieving normalized mean absolute errors (NMAE) of 2.74% for normal force and 2.39% for shear forces.
  • The full image-to-normal-force pipeline is implemented on an FPGA, cutting latency from 3.26 ms on a GPU to 1.09 ms, and the system supports multi-object reconstruction, feedback grasping, and vibration-based dynamic contact sensing.
Source evidence

Abstract

Curved tactile fingertips for dexterous manipulation must resolve fine contact geometry, distinguish normal and tangential loads, and capture transient signals. Existing curved vision-based tactile sensors struggle to combine accurate 3D reconstruction, three-axis force estimation, and high-speed processing in a compact form. This article presents FasTac, a curved vision-based tactile sensor integrating multispectral photometric stereo, dynamic-convolution force estimation, and hardware acceleration on a field-programmable gate array (FPGA). Single-image-sensor simultaneous multispectral imaging provides spatially aligned observations for robust surface normal estimation, followed by boundary-prior fast Poisson depth reconstruction. HyperForce uses position-aware dynamic convolution to model the spatially nonuniform mechanical response of curved elastomers and estimate three-axis forces. The complete image-to-normal-force pipeline is deployed on an FPGA. Experiments show that near-infrared (NIR) illumination and the boundary prior decrease depth mean absolute error (MAE) from 0.2730 mm to 0.0415 mm; HyperForce achieves normalized mean absolute error (NMAE) values of 2.74% and 2.39% for normal and shear forces, respectively; and FPGA deployment shortens processing latency from 3.26 ms on the GPU to 1.09 ms. Multi-object reconstruction, feedback grasping, and vibration measurement validate fine geometric perception, stable force feedback, and dynamic contact sensing.

Comment: 13 pages, 11 figures, including 2 pages of supplementary material. Submitted to IEEE/ASME Transactions on Mechatronics