  Depth estimation attracts widespread attention in the computer vision
community. However, it is still quite difficult to recover an accurate depth
map using only one RGB image. We observe a phenomenon that existing methods
tend to fail in different cases, caused by differences in network architecture,
loss function and so on. In this work, we investigate into the phenomenon and
propose to integrate the strengths of multiple weak depth predictor to build a
comprehensive and accurate depth predictor, which is critical for many
real-world applications, e.g., 3D reconstruction. Specifically, we construct
multiple base (weak) depth predictors by utilizing different Transformer-based
and convolutional neural network (CNN)-based architectures. Transformer
establishes long-range correlation while CNN preserves local information
ignored by Transformer due to the spatial inductive bias. Therefore, the
coupling of Transformer and CNN contributes to the generation of complementary
depth estimates, which are essential to achieve a comprehensive depth
predictor. Then, we design mixers to learn from multiple weak predictions and
adaptively fuse them into a strong depth estimate. The resultant model, which
we refer to as Transformer-assisted depth ensembles (TEDepth). On the standard
NYU-Depth-v2 and KITTI datasets, we thoroughly explore how the neural ensembles
affect the depth estimation and demonstrate that our TEDepth achieves better
results than previous state-of-the-art approaches. To validate the
generalizability across cameras, we directly apply the models trained on
NYU-Depth-v2 to the SUN RGB-D dataset without any fine-tuning, and the superior
results emphasize its strong generalizability.
