# Monocular Reconstruction of Neural Tactile Fields

arXiv: https://arxiv.org/abs/2602.12508 · HTML: https://mlsanity.com/paper/2602.12508/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 42 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Builds on (most cited first, 30 of 33)

- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction](https://mlsanity.com/paper/1905.10711/) (2019)
- [Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers](https://mlsanity.com/paper/2312.09147/) (2023)
- [SparseNeRF: Distilling Depth Ranking for Few-shot Novel View Synthesis](https://mlsanity.com/paper/2303.16196/) (2023)
- [TripoSR: Fast 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/2403.02151/) (2024)
- [TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models](https://mlsanity.com/paper/2502.06608/) (2025)
- [Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer](https://mlsanity.com/paper/2405.14832/) (2024)
- [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)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Intel RealSense Stereoscopic Depth Cameras](https://mlsanity.com/paper/1705.05548/) (2017)
- [3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing](https://mlsanity.com/paper/2410.24091/) (2024)
- [TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors](https://mlsanity.com/paper/2012.08456/) (2020)
- [Self-Supervised Visuo-Tactile Pretraining to Locate and Follow Garment Features](https://mlsanity.com/paper/2209.13042/) (2022)
- [GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger](https://mlsanity.com/paper/2106.08851/) (2021)
- [Active 3D Shape Reconstruction from Vision and Touch](https://mlsanity.com/paper/2107.09584/) (2021)
- [Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2401.12024/) (2024)
- [Tactile-Augmented Radiance Fields](https://mlsanity.com/paper/2405.04534/) (2024)
- [Soft-bubble: A highly compliant dense geometry tactile sensor for robot manipulation](https://mlsanity.com/paper/1904.02252/) (2019)
- [TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction using Vision-Based Tactile Sensing](https://mlsanity.com/paper/2311.12602/) (2023)
- [Recognizing Material Properties from Images](https://mlsanity.com/paper/1801.03127/) (2018)
- [Visual-Tactile Sensing for In-Hand Object Reconstruction](https://mlsanity.com/paper/2303.14498/) (2023)
- [FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback](https://mlsanity.com/paper/2303.07997/) (2023)
- [AcTExplore: Active Tactile Exploration of Unknown Objects](https://mlsanity.com/paper/2310.08745/) (2023)
- [Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects](https://mlsanity.com/paper/2103.00655/) (2021)
- [Pose-Based Tactile Servoing: Controlled Soft Touch using Deep Learning](https://mlsanity.com/paper/2012.02504/) (2020)
- [Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor Based on Finite Element Analysis](https://mlsanity.com/paper/2411.03315/) (2024)
- [Touch2Shape: Touch-Conditioned 3D Diffusion for Shape Exploration and Reconstruction](https://mlsanity.com/paper/2505.13091/) (2025)

## Abstract (arXiv)

Robots operating in the real world must plan through environments that deform, yield, and reconfigure under contact, requiring interaction-aware 3D representations that extend beyond static geometric occupancy. To address this, we introduce neural tactile fields, a novel 3D representation that maps spatial locations to the expected tactile response upon contact. Our model predicts these neural tactile fields from a single monocular RGB image -- the first method to do so. When integrated with off-the-shelf path planners, neural tactile fields enable robots to generate paths that avoid high-resistance objects while deliberately routing through low-resistance regions (e.g. foliage), rather than treating all occupied space as equally impassable. Empirically, our learning framework improves volumetric 3D reconstruction by 85.8% and surface reconstruction by 26.7% compared to state-of-the-art monocular 3D reconstruction methods (LRM and Direct3D).