mlsanityData to 7 Oct 2026

SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completion

arXiv 2209.00381 ↗First posted 1 Sep 2022cs.CV cs.AI cs.LGcited by 5

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Abstract (arXiv)

Holistic scene understanding is pivotal for the performance of autonomous machines. In this paper we propose a new end-to-end model for performing semantic segmentation and depth completion jointly. The vast majority of recent approaches have developed semantic segmentation and depth completion as independent tasks. Our approach relies on RGB and sparse depth as inputs to our model and produces a dense depth map and the corresponding semantic segmentation image. It consists of a feature extractor, a depth completion branch, a semantic segmentation branch and a joint branch which further processes semantic and depth information altogether. The experiments done on Virtual KITTI 2 dataset, demonstrate and provide further evidence, that combining both tasks, semantic segmentation and depth completion, in a multi-task network can effectively improve the performance of each task. Code is available at https://github.com/juanb09111/semantic depth.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 38 of its 44 references that are in the map. First posted on arXiv 1 Sep 2022.

Builds on

38 of 44 references.

References · 38

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26 more
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  19. 2019Dense Depth Posterior (DDP) from Single Image and Sparse Rangecited by 57
  20. 2019Depth Coefficients for Depth Completioncited by 41
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2023RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completioncited by 11
  2. 2022PanDepth: Joint Panoptic Segmentation and Depth Completioncited by 2
  3. 2024Efficient Semantic Splatting for Remote Sensing Multi-view Segmentationcited by 2
  4. 2026GeomPrompt: Geometric Prompt Learning for RGB-D Semantic Segmentation Under Missing and Degraded Depthcited by 1
  5. 2025MT-Depth: Multi-task Instance feature analysis for the Depth Completioncited by 0