  In the domain of multi-baseline stereo, the conventional understanding is
that, in general, increasing baseline separation substantially enhances the
accuracy of depth estimation. However, prevailing self-supervised depth
estimation architectures primarily use minimal frame separation and a
constrained stereo baseline. Larger frame separations can be employed; however,
we show this to result in diminished depth quality due to various factors,
including significant changes in brightness, and increased areas of occlusion.
In response to these challenges, our proposed method, BaseBoostDepth,
incorporates a curriculum learning-inspired optimization strategy to
effectively leverage larger frame separations. However, we show that our
curriculum learning-inspired strategy alone does not suffice, as larger
baselines still cause pose estimation drifts. Therefore, we introduce
incremental pose estimation to enhance the accuracy of pose estimations,
resulting in significant improvements across all depth metrics. Additionally,
to improve the robustness of the model, we introduce error-induced
reconstructions, which optimize reconstructions with added error to the pose
estimations. Ultimately, our final depth network achieves state-of-the-art
performance on KITTI and SYNS-patches datasets across image-based, edge-based,
and point cloud-based metrics without increasing computational complexity at
test time. The project website can be found at
https://kieran514.github.io/BaseBoostDepth-Project.
