TLDR: FINS (Fast Image-to-Neural Surface) is a novel framework that efficiently reconstructs high-fidelity 3D implicit surface models and Signed Distance Fields (SDFs) from just a single RGB image within approximately 10 seconds. It overcomes the limitations of previous methods that required dense multi-view images and long training times, making it highly practical for real-time robotic applications such as obstacle avoidance, path planning, and surface following. FINS achieves its speed and accuracy by integrating pre-trained 3D foundation models for point cloud supervision, a multi-resolution hash grid encoder for efficient feature encoding, and a unique hybrid optimization strategy that combines first-order and approximate second-order methods.
Autonomous robots need to understand their surroundings to navigate and interact safely. A crucial part of this understanding involves creating reliable geometric representations of the environment. Distance-based representations, particularly Signed Distance Fields (SDFs), are widely used in robotics for tasks like motion planning and obstacle avoidance.
Traditional methods for reconstructing implicit surfaces, such as NeuS and its variations, often demand a large number of multi-view images and lengthy training times, sometimes hours. This makes them impractical for real-time applications in robotics where observations might be sparse or limited to a single view.
Introducing FINS: Fast Image-to-Neural Surface
Researchers Wei-Teng Chu, Tianyi Zhang, Matthew Johnson-Roberson, and Weiming Zhi have introduced a new framework called Fast Image-to-Neural Surface (FINS). This lightweight system can reconstruct high-fidelity surfaces and SDF fields from as few as a single image, or a small set of images, within seconds. This breakthrough significantly reduces the dependency on extensive image collections and long training periods, making it highly suitable for real-time robotic deployment.
How FINS Works
FINS integrates several key components to achieve its impressive speed and accuracy:
- 3D Foundation Models: It leverages pre-trained 3D foundation models like DUSt3R and VGGT to convert single-view inputs into 3D point clouds. These point clouds provide crucial supervision for training the SDF.
- Multi-Resolution Hash Grid Encoder: To efficiently capture both coarse and fine geometric details, FINS uses a multi-resolution hash grid encoder. This technique allows for rapid feature encoding and significantly faster convergence compared to older methods.
- Lightweight Geometry and Color Heads: The framework employs separate, lightweight networks for predicting geometry (the signed distance) and color. This separation improves training stability.
- Hybrid Optimization Strategy: FINS uses an efficient staged hybrid optimization scheme. It starts with a standard first-order optimizer (Lion) for a warm-up phase, then switches to a Kronecker-Factored Approximate Curvature (K-FAC) optimizer for the geometry and color heads. K-FAC is an approximate second-order optimizer that accelerates convergence by accounting for the curvature of the loss function, while keeping the overall process efficient.
By combining these elements, FINS can reconstruct detailed geometry and consistent SDFs from a single RGB image in approximately 10 seconds on consumer-grade hardware.
Applications in Robotics
The ability of FINS to quickly generate accurate 3D representations from minimal input has significant implications for autonomous robots. It enables real-time reconstruction and refinement of neural surfaces for various downstream robotics tasks, including:
- Obstacle avoidance
- Path planning
- Surface following
- Robotic inspection (e.g., crack detection)
- Automated surface treatment (painting, polishing)
The paper demonstrates FINS’s applicability for robot surface following tasks, where a robot’s end-effector needs to maintain a specific distance from an object’s surface while moving tangentially. The learned SDFs provide the necessary gradient and iso-surface information for precise control.
Also Read:
- Unlocking Detailed 3D Geometry from Neural Implicit Functions
- How Robots Learn Human-Like Object Manipulation from Video
Performance and Validation
Experiments conducted on datasets like DTU and BlendedMVS show that FINS achieves competitive or superior reconstruction quality compared to state-of-the-art baselines like NeuS, NeuS2, SparseNeuS, and SparseCraft. Crucially, FINS accomplishes this with dramatically fewer input images (down to just one) and significantly shorter training times (around 10 seconds versus minutes or hours for other methods).
An ablation study further confirmed the importance of each component loss term and the optimization strategy in achieving high-quality SDFs. The combination of hash encoding and the hybrid first/second-order optimization proved to be a favorable trade-off between accuracy and speed.
In conclusion, FINS represents a significant step forward in 3D implicit surface reconstruction, offering an efficient and accurate solution that is highly practical for real-time robotic applications. For more details, you can read the full research paper here.
