Incrementally recovering 3D dense structures from monocular videos is of
paramount importance since it enables various robotics and AR applications.
Feature volumes have recently been shown to enable efficient and accurate
incremental dense reconstruction without the need to first estimate depth, but
they are not able to achieve as high of a resolution as depth-based methods due
to the large memory consumption of high-resolution feature volumes. This letter
proposes a real-time feature volume-based dense reconstruction method that
predicts TSDF (Truncated Signed Distance Function) values from a novel
sparsified deep feature volume, which is able to achieve higher resolutions
than previous feature volume-based methods, and is favorable in large-scale
outdoor scenarios where the majority of voxels are empty. An uncertainty-aware
multi-view stereo (MVS) network is leveraged to infer initial voxel locations
of the physical surface in a sparse feature volume. Then for refining the
recovered 3D geometry, deep features are attentively aggregated from multiview
images at potential surface locations, and temporally fused. Besides achieving
higher resolutions than before, our method is shown to produce more complete
reconstructions with finer detail in many cases. Extensive evaluations on both
public and self-collected datasets demonstrate a very competitive real-time
reconstruction result for our method compared to state-of-the-art
reconstruction methods in both indoor and outdoor settings.

本文提出了基于特征体积的实时密集重建方法，采用稀疏的深度特征体积来预测 TSDF 值，通过多视角图像聚合细节信息进行时序融合，实现了比以往更高分辨率和更完整细节的三维几何图形重建，其在室内和室外场景中都比现有的方法具有更好的实时重建性能。