# TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction using Vision-Based Tactile Sensing

arXiv: https://arxiv.org/abs/2311.12602 · HTML: https://mlsanity.com/paper/2311.12602/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 45 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction](https://mlsanity.com/paper/1604.00449/) (2016)
- [Pivotal Tuning for Latent-based Editing of Real Images](https://mlsanity.com/paper/2106.05744/) (2021)
- [What Do Single-view 3D Reconstruction Networks Learn?](https://mlsanity.com/paper/1905.03678/) (2019)
- [Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling](https://mlsanity.com/paper/1804.04610/) (2018)
- [Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation](https://mlsanity.com/paper/2112.05124/) (2021)
- [Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency](https://mlsanity.com/paper/1704.06254/) (2017)
- [DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation](https://mlsanity.com/paper/2005.14679/) (2020)
- [Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations](https://mlsanity.com/paper/2104.01542/) (2021)
- [TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors](https://mlsanity.com/paper/2012.08456/) (2020)
- [3D Shape Reconstruction from Vision and Touch](https://mlsanity.com/paper/2007.03778/) (2020)
- [Active 3D Shape Reconstruction from Vision and Touch](https://mlsanity.com/paper/2107.09584/) (2021)
- [3D Shape Perception from Monocular Vision, Touch, and Shape Priors](https://mlsanity.com/paper/1808.03247/) (2018)
- [Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning](https://mlsanity.com/paper/2110.00792/) (2021)
- [Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections](https://mlsanity.com/paper/2103.16747/) (2021)
- [GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects](https://mlsanity.com/paper/1901.11461/) (2019)
- [VIRDO: Visio-tactile Implicit Representations of Deformable Objects](https://mlsanity.com/paper/2202.00868/) (2022)
- [Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation](https://mlsanity.com/paper/2106.08796/) (2021)
- [Tactile Gym 2.0: Sim-to-real Deep Reinforcement Learning for Comparing Low-cost High-Resolution Robot Touch](https://mlsanity.com/paper/2207.10763/) (2022)
- [Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models](https://mlsanity.com/paper/2304.01182/) (2023)
- [Simulation of Vision-based Tactile Sensors using Physics based Rendering](https://mlsanity.com/paper/2012.13184/) (2020)
- [Generation of GelSight Tactile Images for Sim2Real Learning](https://mlsanity.com/paper/2101.07169/) (2021)

## Built on by (most cited first, 15 of 15)

- [Neural Fields in Robotics: A Survey](https://mlsanity.com/paper/2410.20220/) (2024)
- [Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting](https://mlsanity.com/paper/2403.09875/) (2024)
- [Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces](https://mlsanity.com/paper/2403.20275/) (2024)
- [Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation](https://mlsanity.com/paper/2412.06785/) (2024)
- [GelSLAM: A Real-time, High-Fidelity, and Robust 3D Tactile SLAM System](https://mlsanity.com/paper/2508.15990/) (2025)
- [Touch2Shape: Touch-Conditioned 3D Diffusion for Shape Exploration and Reconstruction](https://mlsanity.com/paper/2505.13091/) (2025)
- [FusionSense: Bridging Common Sense, Vision, and Touch for Robust Sparse-View Reconstruction](https://mlsanity.com/paper/2410.08282/) (2024)
- [A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning](https://mlsanity.com/paper/2409.06912/) (2024)
- [TouchAnything: Diffusion-Guided 3D Reconstruction from Sparse Robot Touches](https://mlsanity.com/paper/2604.08945/) (2026)
- [VisTa3D: A Dataset and Benchmark for Thin Object Reconstruction from Vision, Tactile, and 3D Point Clouds](https://mlsanity.com/paper/2608.20740/) (2026)
- [Object Pose Estimation through Dexterous Touch](https://mlsanity.com/paper/2509.13591/) (2025)
- [Monocular Reconstruction of Neural Tactile Fields](https://mlsanity.com/paper/2602.12508/) (2026)
- [Physically Grounded 3D Generative Reconstruction under Hand Occlusion using Proprioception and Multi-Contact Touch](https://mlsanity.com/paper/2604.09100/) (2026)
- [Proximity3D: Shape from Capacitive Proximity on Sensing Manifold](https://mlsanity.com/paper/2608.30344/) (2026)
- [Calibration-Free Surface Normals Estimation in Vision-Based Tactile Sensing using Universal Photometric Stereo](https://mlsanity.com/paper/2609.31754/) (2026)

## Abstract (arXiv)

Humans rely on their visual and tactile senses to develop a comprehensive 3D understanding of their physical environment. Recently, there has been a growing interest in exploring and manipulating objects using data-driven approaches that utilise high-resolution vision-based tactile sensors. However, 3D shape reconstruction using tactile sensing has lagged behind visual shape reconstruction because of limitations in existing techniques, including the inability to generalise over unseen shapes, the absence of real-world testing, and limited expressive capacity imposed by discrete representations. To address these challenges, we propose TouchSDF, a Deep Learning approach for tactile 3D shape reconstruction that leverages the rich information provided by a vision-based tactile sensor and the expressivity of the implicit neural representation DeepSDF. Our technique consists of two components: (1) a Convolutional Neural Network that maps tactile images into local meshes representing the surface at the touch location, and (2) an implicit neural function that predicts a signed distance function to extract the desired 3D shape. This combination allows TouchSDF to reconstruct smooth and continuous 3D shapes from tactile inputs in simulation and real-world settings, opening up research avenues for robust 3D-aware representations and improved multimodal perception in robotics. Code and supplementary material are available at: https://touchsdf.github.io/