Tactile-Augmented Radiance Fields (2405.04534v1)

Abstract: We present a scene representation, which we call a tactile-augmented radiance field (TaRF), that brings vision and touch into a shared 3D space. This representation can be used to estimate the visual and tactile signals for a given 3D position within a scene. We capture a scene's TaRF from a collection of photos and sparsely sampled touch probes. Our approach makes use of two insights: (i) common vision-based touch sensors are built on ordinary cameras and thus can be registered to images using methods from multi-view geometry, and (ii) visually and structurally similar regions of a scene share the same tactile features. We use these insights to register touch signals to a captured visual scene, and to train a conditional diffusion model that, provided with an RGB-D image rendered from a neural radiance field, generates its corresponding tactile signal. To evaluate our approach, we collect a dataset of TaRFs. This dataset contains more touch samples than previous real-world datasets, and it provides spatially aligned visual signals for each captured touch signal. We demonstrate the accuracy of our cross-modal generative model and the utility of the captured visual-tactile data on several downstream tasks. Project page: https://dou-yiming.github.io/TaRF

• The paper introduces TaRF, which fuses sparse touch data and visual inputs to predict tactile features across unseen 3D regions.
• It employs multi-view geometry and a conditional diffusion model to precisely align and infer tactile information from RGB-D images.
• The approach shows promise for robotics, VR/AR, and design, supported by a dataset of over 19,000 aligned visual-tactile pairs.

Tactile-Augmented Radiance Fields: Merging Touch and Vision in 3D Space

Introduction to TaRF

The concept of a tactile-augmented radiance field (TaRF) merges the sensations of touch and vision within a unified 3D model. By harnessing both sparsely sampled touch data and extensive visual input, TaRF enables the prediction of tactile textures and features across various unseen 3D points in a given space. This multisensory approach not only increases the richness of scene understanding but also addresses some of the practical challenges associated with data acquisition in tactile sensing.

Key Insights and Methodology

Two main insights drive the effectiveness of TaRF:

1. Visual-Tactile Sensor Integration: Leveraging common vision-based touch sensors mounted on cameras, the alignment between touch and visual data is achieved using multi-view geometry techniques. This integration allows for spatial consistency between seen and touched surfaces.
2. Diffusion Models for Touch Prediction: A conditional diffusion model is employed, using RGB-D images derived from neural radiance fields to predict the tactile signal at various points, effectively 'filling in' tactile data between sampled points.

The process involves capturing visuals of a scene with an RGB-D camera while simultaneously recording touch data using a sensor mounted on the camera. The positional data is synthesized into a 3D space, facilitating combined visual-tactile mapping.

Dataset and Findings

The researchers introduced a new dataset featuring 19,300 aligned visual-tactile pairs, surpassing previous datasets in both size and the quality of alignment. Several key findings from the paper include:

• 3D Touch Localization: The model could accurately infer the location in 3D space where specific tactile sensations occur.
• Material Property Analysis: It demonstrated reliable performance in identifying materials based on tactile data, proving its potential utility in automated systems needing to recognize material types or conditions without direct contact.

Practical Implications and Future Directions

Robotics and Physical Simulation

Robots that can 'see' and 'feel' their environments are pivotal for advanced automation. TaRF can potentially enhance robotic perception, allowing for more nuanced interactions with various surfaces and materials, from delicate medical uses to robust industrial applications.

Virtual Reality and Augmented Reality

In VR and AR, creating realistic simulations not just visually but also in terms of how objects feel can significantly enhance user experience. TaRF's ability to predict tactile characteristics in detailed 3D space holds promise for more immersive virtual environments.

Design and Testing

For product design and testing, being able to predict how different parts of an object will feel before physically creating it can save resources and allow for better optimization of materials and ergonomics.

The Road Ahead

While TaRF brings us closer to holistically understanding and interacting with our 3D world through combined senses, its current iteration assumes static scenes and may struggle with materials that change shape or property upon interaction. Future iterations could look into dynamic environments where real-time feedback from touch and visual inputs continually updates the model, increasing both accuracy and application scope.

In conclusion, TaRF presents a compelling development in the fusion of touch and visual data in full 3D scenes, providing a richer, more detailed understanding of our physical environment. Its continued development will likely catalyze innovations across multiple fields, from robotics to interactive digital media.