Recent works on 3D reconstruction from posed images have demonstrated that
direct inference of scene-level 3D geometry without iterative optimization is
feasible using a deep neural network, showing remarkable promise and high
efficiency. However, the reconstructed geometries, typically represented as a
3D truncated signed distance function (TSDF), are often coarse without fine
geometric details. To address this problem, we propose three effective
solutions for improving the fidelity of inference-based 3D reconstructions. We
first present a resolution-agnostic TSDF supervision strategy to provide the
network with a more accurate learning signal during training, avoiding the
pitfalls of TSDF interpolation seen in previous work. We then introduce a depth
guidance strategy using multi-view depth estimates to enhance the scene
representation and recover more accurate surfaces. Finally, we develop a novel
architecture for the final layers of the network, conditioning the output TSDF
prediction on high-resolution image features in addition to coarse voxel
features, enabling sharper reconstruction of fine details. Our method produces
smooth and highly accurate reconstructions, showing significant improvements
across multiple depth and 3D reconstruction metrics.

本文介绍了三种有效的方法来改善基于内推法的三维重建的精度，分别为：分辨率无关的 TSDF 监督策略、使用多视角深度估计的深度引导策略和使用高分辨率图像特征的新颖体系结构。这些方法可以使三维重建更加精细准确。