In this paper, we introduce a novel approach that harnesses both 2D and 3D
attentions to enable highly accurate depth completion without requiring
iterative spatial propagations. Specifically, we first enhance a baseline
convolutional depth completion model by applying attention to 2D features in
the bottleneck and skip connections. This effectively improves the performance
of this simple network and sets it on par with the latest, complex
transformer-based models. Leveraging the initial depths and features from this
network, we uplift the 2D features to form a 3D point cloud and construct a 3D
point transformer to process it, allowing the model to explicitly learn and
exploit 3D geometric features. In addition, we propose normalization techniques
to process the point cloud, which improves learning and leads to better
accuracy than directly using point transformers off the shelf. Furthermore, we
incorporate global attention on downsampled point cloud features, which enables
long-range context while still being computationally feasible. We evaluate our
method, DeCoTR, on established depth completion benchmarks, including NYU Depth
V2 and KITTI, showcasing that it sets new state-of-the-art performance. We
further conduct zero-shot evaluations on ScanNet and DDAD benchmarks and
demonstrate that DeCoTR has superior generalizability compared to existing
approaches.

通过使用 2D 和 3D 注意力，提出了一种新颖的方法，实现了高精度的深度完成，无需迭代空间传播，该方法在深度完成模型中应用 2D 特征和 3D 点云处理，从而达到了与基于变压器的模型相当的性能，并通过正规化技术和全局注意力改进了学习和准确性。