Khan, M.A.U.; Nazir, D.; Pagani, A.; Mokayed, H.; Liwicki, M.; Stricker, D.; Afzal, M.Z. A Comprehensive Survey of Depth Completion Approaches. Sensors2022, 22, 6969.
Khan, M.A.U.; Nazir, D.; Pagani, A.; Mokayed, H.; Liwicki, M.; Stricker, D.; Afzal, M.Z. A Comprehensive Survey of Depth Completion Approaches. Sensors 2022, 22, 6969.
Khan, M.A.U.; Nazir, D.; Pagani, A.; Mokayed, H.; Liwicki, M.; Stricker, D.; Afzal, M.Z. A Comprehensive Survey of Depth Completion Approaches. Sensors2022, 22, 6969.
Khan, M.A.U.; Nazir, D.; Pagani, A.; Mokayed, H.; Liwicki, M.; Stricker, D.; Afzal, M.Z. A Comprehensive Survey of Depth Completion Approaches. Sensors 2022, 22, 6969.
Abstract
Depth maps produced by LiDAR based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse depth map. While the traditional approaches focus on directly completing this sparsity from the sparse depth maps, modern techniques use RGB images as a guidance tool to resolve this problem. Whilst many others rely on affinity matrices for depth completion. Based on these approaches, we have sub-divided the literature into two major categories; traditional approaches and backbone-based approaches. The latter is further sub-divided into two-branch, and spatial propagation approaches. The two-branch approaches still have a sub-category named guided-kernel approaches. In this paper, for the first time ever we present a comprehensive survey of depth completion methods. We present a novel taxonomy of depth completion approaches, review and detail different state-of-the art techniques within each category for depth completion of LiDAR data, and provide quantitative results for the approaches on KITTI and NYUv2 depth completion benchmark datasets.
Computer Science and Mathematics, Computer Vision and Graphics
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