Attention-based Context Aggregation Network for Monocular Depth Estimation

Understand

Depth estimation is a traditional computer vision task, which plays a crucial role in understanding 3D scene geometry.

• Recently, deep-convolutional-neural-networks based methods have achieved promising results in the monocular depth estimation field.
• Specifically, the framework that combines the multi-scale features extracted by the dilated convolution based block (atrous spatial pyramid pooling, ASPP) has gained the significant improvement in the dense labeling task.
• However, the discretized and predefined dilation rates cannot capture the continuous context information that differs in diverse scenes and easily introduce the grid artifacts in depth estimation.