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A Comprehensive Survey of Depth Completion Approaches.
- Source :
-
Sensors (14248220) . Sep2022, Vol. 22 Issue 18, p6969-N.PAG. 18p. - Publication Year :
- 2022
-
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 earlier approaches focused 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 divided the literature into two major categories; unguided methods and image-guided methods. The latter is further subdivided into multi-branch and spatial propagation networks. The multi-branch networks further have a sub-category named image-guided filtering. 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 in 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *LIDAR
*DETECTORS
*TAXONOMY
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 22
- Issue :
- 18
- Database :
- Academic Search Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 159357427
- Full Text :
- https://doi.org/10.3390/s22186969