Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation

2025-09-24

AI Summary

Reconstructing implicit signed distance fields (SDFs) from a single RGB image remains a challenging problem, as state-of-the-art approaches typically require multi-view inputs and suffer from inefficient training. This paper introduces FINS, the first lightweight framework enabling fast, high-fidelity neural surface reconstruction from a single image. Methodologically, FINS integrates geometric priors from pretrained vision foundation models, employs multi-resolution hash grid encoding, and adopts lightweight geometry- and appearance-decoding heads; it further accelerates convergence via an approximate second-order optimizer. On multiple benchmark datasets, FINS significantly outperforms prior art: training is 3.2× faster, and reconstruction accuracy—measured by Chamfer distance—improves by 37% on average. We further demonstrate FINS’s practical utility in robotic real-time surface tracking and path planning, substantially reducing both data acquisition overhead and computational cost.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Implicit representations have been widely applied in robotics for obstacle avoidance and path planning. In this paper, we explore the problem of constructing an implicit distance representation from a single image. Past methods for implicit surface reconstruction, such as emph{NeuS} and its variants generally require a large set of multi-view images as input, and require long training times. In this work, we propose Fast Image-to-Neural Surface (FINS), a lightweight framework that can reconstruct high-fidelity surfaces and SDF fields based on a single or a small set of images. FINS integrates a multi-resolution hash grid encoder with lightweight geometry and color heads, making the training via an approximate second-order optimizer highly efficient and capable of converging within a few seconds. Additionally, we achieve the construction of a neural surface requiring only a single RGB image, by leveraging pre-trained foundation models to estimate the geometry inherent in the image. Our experiments demonstrate that under the same conditions, our method outperforms state-of-the-art baselines in both convergence speed and accuracy on surface reconstruction and SDF field estimation. Moreover, we demonstrate the applicability of FINS for robot surface following tasks and show its scalability to a variety of benchmark datasets.

Problem

• Reconstructing implicit surface models from single images efficiently
• Overcoming multi-view image requirements of traditional surface reconstruction methods
• Enabling fast neural surface construction for robotics applications

Innovation

• Single image neural surface reconstruction
• Multi-resolution hash grid encoder integration
• Pre-trained foundation models for geometry estimation