Dense 3D reconstruction from a stream of depth images is the key to many
mixed reality and robotic applications. Although methods based on Truncated
Signed Distance Function (TSDF) Fusion have advanced the field over the years,
the TSDF volume representation is confronted with striking a balance between
the robustness to noisy measurements and maintaining the level of detail. We
present Bi-level Neural Volume Fusion (BNV-Fusion), which leverages recent
advances in neural implicit representations and neural rendering for dense 3D
reconstruction. In order to incrementally integrate new depth maps into a
global neural implicit representation, we propose a novel bi-level fusion
strategy that considers both efficiency and reconstruction quality by design.
We evaluate the proposed method on multiple datasets quantitatively and
qualitatively, demonstrating a significant improvement over existing methods.

本篇论文提出了一种基于神经隐式表达和神经渲染的双层神经体积融合方法，用于增量式地将新的深度图像融合到全局神经隐式表示中，以实现密集的 3D 重建，评估表明此方法在多个数据集上均得到了显著的改进。