Multi-Resolution Shared Representative Filtering for Real-Time Depth Completion

Abstract

We present shared representative filtering for real-time high-resolution depth completion with RGB-D sensors. Conventional filtering-based methods face a dilemma when the missing regions of the depth map are large. When the filter window is small, the filter fails to include enough samples. On the other hand, when the window is large, the method could oversmooth depth boundaries due to the error introduced by the extra samples. Our method adapts the filter kernels to the shape of the missing regions to collect a sufficient number of samples while avoiding oversmoothing. We collect depth samples by searching for a small set of similar pixels, which we call the representatives, using an efficient line search algorithm. We then combine the representatives using a joint bilateral weight. Experiments show that our method can filter a high-resolution depth map within a few milliseconds while outperforming previous filtering-based methods on both real-world and synthetic data in terms of both efficiency and accuracy, especially when dealing with large missing regions in depth maps.