HRBF-Fusion: Accurate 3D reconstruction from RGB-D data using on-the-fly implicits
This work addresses the issue of error accumulation and distortion in 3D reconstruction for applications like computer vision and robotics, representing an incremental improvement over existing RGB-D fusion methods.
The paper tackles the problem of inaccurate 3D reconstruction from RGB-D data by introducing a method using on-the-fly Hermite Radial Basis Functions (HRBFs) as a continuous surface representation, which outperforms state-of-the-art approaches in tracking robustness and reconstruction accuracy.
Reconstruction of high-fidelity 3D objects or scenes is a fundamental research problem. Recent advances in RGB-D fusion have demonstrated the potential of producing 3D models from consumer-level RGB-D cameras. However, due to the discrete nature and limited resolution of their surface representations (e.g., point- or voxel-based), existing approaches suffer from the accumulation of errors in camera tracking and distortion in the reconstruction, which leads to an unsatisfactory 3D reconstruction. In this paper, we present a method using on-the-fly implicits of Hermite Radial Basis Functions (HRBFs) as a continuous surface representation for camera tracking in an existing RGB-D fusion framework. Furthermore, curvature estimation and confidence evaluation are coherently derived from the inherent surface properties of the on-the-fly HRBF implicits, which devote to a data fusion with better quality. We argue that our continuous but on-the-fly surface representation can effectively mitigate the impact of noise with its robustness and constrain the reconstruction with inherent surface smoothness when being compared with discrete representations. Experimental results on various real-world and synthetic datasets demonstrate that our HRBF-fusion outperforms the state-of-the-art approaches in terms of tracking robustness and reconstruction accuracy.