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

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

Xiaofan Lu, Kaiji Huang, Jiahui Chen, Yuankai Lin, Hua Yang, Zhouping Yin · N/A · 2026

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Summary

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...

Abstract Summary

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.

Key Points

  • Curved tactile fingertips for dexterous manipulation must resolve fine contact geometry, distingu...
  • Existing curved vision-based tactile sensors struggle to combine accurate 3D reconstruction, thre...
  • This article presents FasTac, a curved vision-based tactile sensor integrating multispectral phot...
  • Single-image-sensor simultaneous multispectral imaging provides spatially aligned observations fo...
  • HyperForce uses position-aware dynamic convolution to model the spatially nonuniform mechanical r...

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

|Authors: Xiaofan Lu, Kaiji Huang, Jiahui Chen, Yuankai Lin, Hua Yang, Zhouping Yin

|Venue: arXiv preprint | Year: 2026

|arXiv: 2607.28416v1

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.

Key Contributions

  • Curved tactile fingertips for dexterous manipulation must resolve fine contact geometry, distingu…
  • Existing curved vision-based tactile sensors struggle to combine accurate 3D reconstruction, thre…
  • This article presents FasTac, a curved vision-based tactile sensor integrating multispectral phot…
  • Single-image-sensor simultaneous multispectral imaging provides spatially aligned observations fo…
  • HyperForce uses position-aware dynamic convolution to model the spatially nonuniform mechanical r…

Topics

  • manipulation
  • vision
  • tactile

Code & Data

No code repository linked in paper metadata.

BibTeX

@article{Lu2026_260728416v1,
  title     = {FasTac: A Curved Multispectral Vision-Based Tactile Sensor for High-Speed High-Precision 3D Shape and Force Perception},
  author    = {Xiaofan Lu and Kaiji Huang and Jiahui Chen and Yuankai Lin and Hua Yang and Zhouping Yin},
  year      = {2026},
  eprint    = {2607.28416v1},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url       = {https://arxiv.org/abs/2607.28416v1}
}
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