Self-supervised Monocular Depth Estimation Method Based on Piecewise Plane Model

Weiwei Zhang, Guanwen Zhang, Wei Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Monocular depth estimation is crucial in scene understanding and autonomous driving. Recently, a considerable number of methods based on deep learning and piecewise plane geometric priors have made significant progress. However, these methods still face the following issues: 1) Current plane segmentation methods based on pixel color and object contours often yield sub-planes that are mostly curved surfaces, making direct fitting with a single plane model ineffective. 2) Plane prior knowledge ignores the depth distribution of pixels at sub-plane edges, leading to poor depth estimation at these edges.3) Existing methods often rely on difficult-to-obtain depth ground truth as supervision signals. To address the aforementioned issues, we propose a self-supervised monocular depth estimation method based on monocular video. We introduce a multi-plane fusion approach to fit sub-planes in images. We model the pixel depth at sub-plane edges as bimodal distribution and design a dynamic search method to enhance the computation efficiency of the cost volume. We validate the performance of our proposed method on the KITTI and NYU-Depth-v2 datasets.

Original languageEnglish
Title of host publication2024 IEEE 19th Conference on Industrial Electronics and Applications, ICIEA 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350360868
DOIs
StatePublished - 2024
Event19th IEEE Conference on Industrial Electronics and Applications, ICIEA 2024 - Kristiansand, Norway
Duration: 5 Aug 20248 Aug 2024

Publication series

Name2024 IEEE 19th Conference on Industrial Electronics and Applications, ICIEA 2024

Conference

Conference19th IEEE Conference on Industrial Electronics and Applications, ICIEA 2024
Country/TerritoryNorway
CityKristiansand
Period5/08/248/08/24

Keywords

  • Deep learning
  • Planarity prior
  • Self-supervised monocular depth estimation

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