PROFusion: Robust and Accurate Dense Reconstruction via Camera Pose Regression and Optimization

Abstract-- Real-time dense scene reconstruction during unstable camera motions is crucial for robotics, yet current RGB-D SLAM systems fail when cameras experience large viewpoint changes, fast motions, or sudden shaking. Classical optimization-based methods deliver high accuracy but fail with poor initialization during large motions, while learning-based approaches provide robustness but lack sufficient accuracy for dense reconstruction. We address this challenge through a combination of learning-based initialization with optimization-based refinement. Our method employs a camera pose regression network to predict metric-aware relative poses from consecutive RGB-D frames, which serve as reliable starting points for a randomized optimization algorithm that further aligns depth images with the scene geometry. Extensive experiments demonstrate promising results: our approach outperforms the best competitor on challenging benchmarks, while maintaining comparable accuracy on stable motion sequences. The system operates in real-time, showcasing that combining simple and principled techniques can achieve both robustness for unstable motions and accuracy for dense reconstruction. I. INTRODUCTION Real-time camera tracking and dense scene reconstruction are fundamental problems in robotics and computer vision. For autonomous robots, handling unstable camera motions is both challenging and critical. Current RGB-D SLAM (Simultaneous Localization and Mapping) systems perform well in controlled environments with smooth, typically slow camera movements.