Sparse laser observation-based image depth estimation method

Abstract

The invention discloses a sparse laser observation-based image depth estimation method. The method proposes that monocular image-based depth dense reconstruction is realized by utilizing sparse observation of single-line laser or multi-line laser. A deep neural network is trained in a mode of constructing a reference depth map and a residual error depth map, and sparse partial observation depth information is fully utilized. Compared with a method for performing depth estimation only by using a monocular image, the method provided by the invention has remarkable advantages.

Description

technical field [0001] The invention relates to the field of scene depth estimation, in particular to a scene dense depth estimation method based on monocular images and sparse lasers. Background technique [0002] Based on rich experience and continuous learning, human beings also have the ability to estimate the distance of objects in the image from monocular images, that is, the ability to estimate depth to a certain extent. In recent years, machine learning methods have also made remarkable progress in imitating the depth estimation ability of humans, especially data-driven deep learning techniques. This technique avoids the manual feature design process, learns features based on the original monocular RGB image, and outputs a prediction of the corresponding depth of the image. [0003] Eigen et al. first proposed deep learning-based monocular depth estimation. They constructed a two-stage depth estimation network. The first stage generates a rough estimate and the seco...

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Problems solved by technology

Eigen et al. also mentioned that there may be a global bias in depth estimation based on monocular images

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