  n this paper, we propose an effective and efficient pyramid multi-view stereo
(MVS) net with self-adaptive view aggregation for accurate and complete dense
point cloud reconstruction. Different from using mean square variance to
generate cost volume in previous deep-learning based MVS methods, our
\textbf{VA-MVSNet} incorporates the cost variances in different views with
small extra memory consumption by introducing two novel self-adaptive view
aggregations: pixel-wise view aggregation and voxel-wise view aggregation. To
further boost the robustness and completeness of 3D point cloud reconstruction,
we extend VA-MVSNet with pyramid multi-scale images input as
\textbf{PVA-MVSNet}, where multi-metric constraints are leveraged to aggregate
the reliable depth estimation at the coarser scale to fill in the mismatched
regions at the finer scale. Experimental results show that our approach
establishes a new state-of-the-art on the \textsl{\textbf{DTU}} dataset with
significant improvements in the completeness and overall quality, and has
strong generalization by achieving a comparable performance as the
state-of-the-art methods on the \textsl{\textbf{Tanks and Temples}} benchmark.
Our codebase is at
\hyperlink{https://github.com/yhw-yhw/PVAMVSNet}{https://github.com/yhw-yhw/PVAMVSNet}
