Pixelwise Adaptive Discretization with Uncertainty Sampling for Depth Completion

Image guided depth completion is an extensively studied multi-modal task that takes sparse measurements and RGB images as input to recover dense depth maps. While the common practice is to regress the depth value from the unbounded range, some recent methods achieve breakthrough performance by discretizing the regression range into a number of discrete depth values, namely, Depth Hypotheses, and casting the scalar regression to the distribution estimation. However, existing methods employ the handcraft or image-level adaptive discretization strategies, where their generated depth hypotheses are pixel-shared, which can not adapt to all pixels and is inefficient. In this paper, we are the first to consider the difference between pixels and propose Pixelwise Adaptive Discretization to generate the tailored depth hypotheses for each pixel. Meanwhile, we introduce Uncertainty Sampling to generate the compact depth hypotheses for easy pixels and loose for hard pixels. This divide-and-conquer for each pixel allows the discrete depth hypotheses to be concentrated around the ground-truth of each pixel as much as possible, which is the core of discretization methods. Extensive experiments on the outdoor KITTI and indoor NYU Depth V2 datasets show that our model, called PADNet, surpasses the previous state-of-the-art methods even with limited parameters and computational cost.