A 3D-Printable Dataset for Fair Testing and Comparisons of Tactile Sensors
- URL: http://arxiv.org/abs/2606.25886v1
- Date: Wed, 24 Jun 2026 14:27:16 GMT
- Title: A 3D-Printable Dataset for Fair Testing and Comparisons of Tactile Sensors
- Abstract summary: Existing texture datasets for tactile sensing primarily consist of sensor readings from a specific sensor interacting with available surfaces/objects.<n>We introduce a 3D-printable dataset of mathematically defined textures designed to be fabricated reliably across different printers and filament types.
- Score: 0.5803335081904312
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Existing texture datasets for tactile sensing primarily consist of sensor readings from a specific sensor interacting with available surfaces/objects rather than describing the textures themselves, limiting fair comparison between tactile sensors and hindering reproducible research. In this work, we introduce a 3D-printable dataset of mathematically defined textures designed to be fabricated reliably across different printers and filament types. The dataset consists of six parametrically generated surface patterns derived from combinations of sine-wave and Fourier-based functions, giving controlled variation in spatial frequency, amplitude, and directional structure. We evaluate the reproducibility of these textures across three popular 3D printers and multiple filament types by measuring variance in images captured using an optical TacTip sensor under controlled contact conditions. Our results show that print quality, particularly peak sharpness and stringing, affects tactile variance, with higher-end printers producing significantly more consistent signatures. Classification experiments using neural networks and PCA-based models further demonstrate that high-quality prints support strong within-printer generalisation, while cross-printer generalisation remains challenging due to geometric inconsistencies. This work establishes the first openly available, physically reproducible 3D-printed texture benchmark, providing a foundation for fair comparison of tactile sensors.
Related papers
- Talk2Sensors: 3D Visual Grounding in Autonomous Driving via Sensor-Adaptive Physical Cue Matching [66.91228384649921]
We introduce Talk2Sensors, the first multi-sensor 3D visual grounding dataset built upon camera, LiDAR, and 4D radar.<n>We propose TSFormer, a unified Transformer-based framework for language-guided 3D visual grounding in autonomous driving.
arXiv Detail & Related papers (2026-08-05T07:58:35Z) - Text-based Tactile Graphics Generation for the Visually Impaired [51.53004839882403]
Tactile graphics are a primary medium for blind and low-vision (BLV) individuals to access non-textual information.<n>We present the first integrated generative system that produces fabrication-ready 2.5D tactile graphics directly from natural language prompts.
arXiv Detail & Related papers (2026-07-10T03:04:30Z) - ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing [0.4666493857924357]
This work introduces ShapeGen3DCP, a deep learning framework for prediction of filament cross-sectional geometry in 3D Concrete Printing (3DCP)<n>The framework is based on a neural network architecture that takes as input both material properties in the fluid state (density, yield stress, plastic viscosity) and process parameters (nozzle diameter, nozzle height, printing and flow velocities) to directly predict extruded layer shapes.<n>The training dataset was synthetically generated using a well-established Particle Finite Element (PFEM) model of 3DCP, overcoming the scarcity of experimental data.
arXiv Detail & Related papers (2025-10-02T13:30:20Z) - Textured Mesh Saliency: Bridging Geometry and Texture for Human Perception in 3D Graphics [50.23625950905638]
We present a new dataset for textured mesh saliency, created through an innovative eye-tracking experiment in a six degrees of freedom (6-DOF) VR environment.<n>Our proposed model predicts saliency maps for textured mesh surfaces by treating each triangular face as an individual unit and assigning a saliency density value to reflect the importance of each local surface region.
arXiv Detail & Related papers (2024-12-11T08:27:33Z) - 3D object quality prediction for Metal Jet Printer with Multimodal thermal encoder [46.85584046139531]
Various factors during metal printing affect the printed parts' quality.
With the large data gathered from HP's MetJet printing process, AI techniques can be used to analyze, learn, and effectively infer the printed part quality metrics.
arXiv Detail & Related papers (2024-04-17T21:57:29Z) - CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs [65.80187860906115]
We propose a novel approach to improve NeRF's performance with sparse inputs.
We first adopt a voxel-based ray sampling strategy to ensure that the sampled rays intersect with a certain voxel in 3D space.
We then randomly sample additional points within the voxel and apply a Transformer to infer the properties of other points on each ray, which are then incorporated into the volume rendering.
arXiv Detail & Related papers (2024-03-25T15:56:17Z) - Machine Learning Based Compensation for Inconsistencies in Knitted Force
Sensors [1.0742675209112622]
Knitted sensors frequently suffer from inconsistencies due to innate effects such as offset, relaxation, and drift.
In this paper, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN)
By training a three-layer ANN with a total of 8 neurons, we manage to significantly improve the mapping between sensor reading and actuation force.
arXiv Detail & Related papers (2023-06-21T09:19:33Z) - On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks [61.74608497496841]
Training on inaccurate or corrupt data induces model bias and hampers generalisation capabilities.
This paper investigates the effect of sensor errors for the dense 3D vision tasks of depth estimation and reconstruction.
arXiv Detail & Related papers (2023-03-26T22:32:44Z) - Towards Multidimensional Textural Perception and Classification Through
Whisker [0.0]
Whisker-based multidimensional surface texture data is missing in the literature.
We present a novel sensor design to acquire multidimensional texture information.
We experimentally validate that the sensor can classify texture with roughness as low as $2.5mu m$ at an accuracy of $90%$ or more.
arXiv Detail & Related papers (2022-09-01T11:14:17Z) - Characterization of 3D Printers and X-Ray Computerized Tomography [3.333810206561284]
Thirty-eight samples are printed using four commercially available 3D printers, namely: (a) Ultimaker 2 Extended+, (b) Delta Wasp, (c) Raise E2, and (d) ProJet MJP.
The sample profiles contain uniform and non-uniform distribution of the assorted size of cubes and spheres with a known amount of porosity.
It is found that ProJet MJP gives the best quality of printed samples with the least amount of surface roughness and almost near to the actual porosity value.
arXiv Detail & Related papers (2022-05-27T11:06:08Z) - Elastic Tactile Simulation Towards Tactile-Visual Perception [58.44106915440858]
We propose Elastic Interaction of Particles (EIP) for tactile simulation.
EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact.
We further propose a tactile-visual perception network that enables information fusion between tactile data and visual images.
arXiv Detail & Related papers (2021-08-11T03:49:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.