Depth Completion deals with the problem of converting a sparse depth map to a
dense one, given the corresponding color image. Convolutional spatial
propagation network (CSPN) is one of the state-of-the-art (SoTA) methods of
depth completion, which recovers structural details of the scene. In this
paper, we propose CSPN++, which further improves its effectiveness and
efficiency by learning adaptive convolutional kernel sizes and the number of
iterations for the propagation, thus the context and computational resources
needed at each pixel could be dynamically assigned upon requests. Specifically,
we formulate the learning of the two hyper-parameters as an architecture
selection problem where various configurations of kernel sizes and numbers of
iterations are first defined, and then a set of soft weighting parameters are
trained to either properly assemble or select from the pre-defined
configurations at each pixel. In our experiments, we find weighted assembling
can lead to significant accuracy improvements, which we referred to as
"context-aware CSPN", while weighted selection, "resource-aware CSPN" can
reduce the computational resource significantly with similar or better
accuracy. Besides, the resource needed for CSPN++ can be adjusted w.r.t. the
computational budget automatically. Finally, to avoid the side effects of noise
or inaccurate sparse depths, we embed a gated network inside CSPN++, which
further improves the performance. We demonstrate the effectiveness of CSPN++on
the KITTI depth completion benchmark, where it significantly improves over CSPN
and other SoTA methods.

本文提出 CSPN++ 方法，通过学习自适应卷积核大小和迭代传播次数的超参数，使得每个像素点都能动态分配上下文和计算资源，分别提出了 “上下文感知 CSPN” 和 “资源感知 CSPN”，在 KITTI 深度完成基准测试中取得了显著的精度和计算资源改进。