  In this paper, we propose an efficient and effective dense hybrid recurrent
multi-view stereo net with dynamic consistency checking, namely
$D^{2}$HC-RMVSNet, for accurate dense point cloud reconstruction. Our novel
hybrid recurrent multi-view stereo net consists of two core modules: 1) a light
DRENet (Dense Reception Expanded) module to extract dense feature maps of
original size with multi-scale context information, 2) a HU-LSTM (Hybrid
U-LSTM) to regularize 3D matching volume into predicted depth map, which
efficiently aggregates different scale information by coupling LSTM and U-Net
architecture. To further improve the accuracy and completeness of reconstructed
point clouds, we leverage a dynamic consistency checking strategy instead of
prefixed parameters and strategies widely adopted in existing methods for dense
point cloud reconstruction. In doing so, we dynamically aggregate geometric
consistency matching error among all the views. Our method ranks
\textbf{$1^{st}$} on the complex outdoor \textsl{Tanks and Temples} benchmark
over all the methods. Extensive experiments on the in-door DTU dataset show our
method exhibits competitive performance to the state-of-the-art method while
dramatically reduces memory consumption, which costs only $19.4\%$ of R-MVSNet
memory consumption. The codebase is available at
\hyperlink{https://github.com/yhw-yhw/D2HC-RMVSNet}{https://github.com/yhw-yhw/D2HC-RMVSNet}.
