  Pairwise matching cost aggregation is a crucial step for modern
learning-based Multi-view Stereo (MVS). Prior works adopt an early aggregation
scheme, which adds up pairwise costs into an intermediate cost. However, we
analyze that this process can degrade informative pairwise matchings, thereby
blocking the depth network from fully utilizing the original geometric matching
cues. To address this challenge, we present a late aggregation approach that
allows for aggregating pairwise costs throughout the network feed-forward
process, achieving accurate estimations with only minor changes of the plain
CasMVSNet. Instead of building an intermediate cost by weighted sum, late
aggregation preserves all pairwise costs along a distinct view channel. This
enables the succeeding depth network to fully utilize the crucial geometric
cues without loss of cost fidelity. Grounded in the new aggregation scheme, we
propose further techniques addressing view order dependence inside the
preserved cost, handling flexible testing views, and improving the depth
filtering process. Despite its technical simplicity, our method improves
significantly upon the baseline cascade-based approach, achieving comparable
results with state-of-the-art methods with favorable computation overhead.
