# ShapeMap 3-D: Efficient shape mapping through dense touch and vision

arXiv: https://arxiv.org/abs/2109.09884 · HTML: https://mlsanity.com/paper/2109.09884/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2021.

## Built on by (most cited first, 30 of 31)

- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [General In-Hand Object Rotation with Vision and Touch](https://mlsanity.com/paper/2309.09979/) (2023)
- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [Learning Visuotactile Skills with Two Multifingered Hands](https://mlsanity.com/paper/2404.16823/) (2024)
- [See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation](https://mlsanity.com/paper/2212.03858/) (2022)
- [The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects](https://mlsanity.com/paper/2306.00956/) (2023)
- [MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World](https://mlsanity.com/paper/2401.08577/) (2024)
- [Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations](https://mlsanity.com/paper/2408.04380/) (2024)
- [MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation](https://mlsanity.com/paper/2310.16917/) (2023)
- [Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation](https://mlsanity.com/paper/2312.13469/) (2023)
- [Neural Contact Fields: Tracking Extrinsic Contact with Tactile Sensing](https://mlsanity.com/paper/2210.09297/) (2022)
- [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)
- [Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces](https://mlsanity.com/paper/2403.20275/) (2024)
- [FeelAnyForce: Estimating Contact Force Feedback from Tactile Sensation for Vision-Based Tactile Sensors](https://mlsanity.com/paper/2410.02048/) (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)
- [Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing](https://mlsanity.com/paper/2210.04026/) (2022)
- [V-HOP: Visuo-Haptic 6D Object Pose Tracking](https://mlsanity.com/paper/2502.17434/) (2025)
- [ViTa-Zero: Zero-shot Visuotactile Object 6D Pose Estimation](https://mlsanity.com/paper/2504.13179/) (2025)
- [FusionSense: Bridging Common Sense, Vision, and Touch for Robust Sparse-View Reconstruction](https://mlsanity.com/paper/2410.08282/) (2024)
- [RA-Touch: Retrieval-Augmented Touch Understanding with Enriched Visual Data](https://mlsanity.com/paper/2505.14270/) (2025)
- [Learning Tactile Insertion in the Real World](https://mlsanity.com/paper/2405.00383/) (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)
- [A Modularized Design Approach for GelSight Family of Vision-based Tactile Sensors](https://mlsanity.com/paper/2504.14739/) (2025)
- [Object Pose Estimation through Dexterous Touch](https://mlsanity.com/paper/2509.13591/) (2025)
- [Physically Grounded 3D Generative Reconstruction under Hand Occlusion using Proprioception and Multi-Contact Touch](https://mlsanity.com/paper/2604.09100/) (2026)
- [Objects as Audio-Visual Modal Sound Fields](https://mlsanity.com/paper/2608.05145/) (2026)

## Abstract (arXiv)

Knowledge of 3-D object shape is of great importance to robot manipulation tasks, but may not be readily available in unstructured environments. While vision is often occluded during robot-object interaction, high-resolution tactile sensors can give a dense local perspective of the object. However, tactile sensors have limited sensing area and the shape representation must faithfully approximate non-contact areas. In addition, a key challenge is efficiently incorporating these dense tactile measurements into a 3-D mapping framework. In this work, we propose an incremental shape mapping method using a GelSight tactile sensor and a depth camera. Local shape is recovered from tactile images via a learned model trained in simulation. Through efficient inference on a spatial factor graph informed by a Gaussian process, we build an implicit surface representation of the object. We demonstrate visuo-tactile mapping in both simulated and real-world experiments, to incrementally build 3-D reconstructions of household objects.