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MASC-Net: Multi-scale Anisotropic Sparse Convolutional Network for Sparse Depth Densification

Irregular sparse depth densification has attracted a significant amount of recent interests in computer vision, robotics, autonomous driving and augmented reality applications. Low-cost 3D sensors available on the market today produce sparse and irregular depth data resulting in sometimes inaccurate or inconsistent results. This paper proposes a novel approach for sparse depth densification called the Multi-scale Anisotropic Sparse Convolutional Network (MASC-Net). Conventional sparse convolutional approaches face two main challenges in deep neural networks: 1) blurry depth and fattening artifacts near object boundaries due to isotropic validity masks, and 2) the validity mask mismatch problem across different layers. To address these problems, we propose anisotropic sparse convolutional layers based on spatially varying validity masks with guidance features. We also propose Validity-Aware Modules (VAMs) to resolve the validity mask mismatch problem, which enables many modern deep learning components to be applied to sparse data. Further, a multi-scale completion module is proposed to utilize multiple scales of context to fill the missing information. Experimental results show that MASC-Net outperforms the state-of-the-art depth densification methods on the KITTI depth completion benchmark in both quantitative and qualitative measures.