RemixFusion: Residual-based Mixed Representation for Large-scale Online RGB-D Reconstruction

TL;DR

RemixFusion introduces a residual-based mixed scene representation combining explicit TSDF grids with implicit neural residuals, enabling high-quality, large-scale online RGB-D reconstruction with efficient pose estimation.

Contribution

The paper proposes a novel residual-based mixed representation and a pose optimization method that significantly improve large-scale online RGB-D reconstruction quality and efficiency.

Findings

Outperforms state-of-the-art methods in mapping accuracy.

Achieves better camera pose estimation in large-scale scenes.

Efficient online learning with divide-and-conquer scene factorization.

Abstract

The introduction of the neural implicit representation has notably propelled the advancement of online dense reconstruction techniques. Compared to traditional explicit representations, such as TSDF, it improves the mapping completeness and memory efficiency. However, the lack of reconstruction details and the time-consuming learning of neural representations hinder the widespread application of neural-based methods to large-scale online reconstruction. We introduce RemixFusion, a novel residual-based mixed representation for scene reconstruction and camera pose estimation dedicated to high-quality and large-scale online RGB-D reconstruction. In particular, we propose a residual-based map representation comprised of an explicit coarse TSDF grid and an implicit neural module that produces residuals representing fine-grained details to be added to the coarse grid. Such mixed representation allows for detail-rich reconstruction with bounded time and memory budget, contrasting with the overly-smoothed results by the purely implicit representations, thus paving the way for high-quality camera tracking. Furthermore, we extend the residual-based representation to handle multi-frame joint pose optimization via bundle adjustment (BA). In contrast to the existing methods, which optimize poses directly, we opt to optimize pose changes. Combined with a novel technique for adaptive gradient amplification, our method attains better optimization convergence and global optimality. Furthermore, we adopt a local moving volume to factorize the mixed scene representation with a divide-and-conquer design to facilitate efficient online learning in our residual-based framework. Extensive experiments demonstrate that our method surpasses all state-of-the-art ones, including those based either on explicit or implicit representations, in terms of the accuracy of both mapping and tracking on large-scale scenes.