Monocular Depth Estimation Using Cues Inspired by Biological Vision Systems

TL;DR

This paper introduces a biologically inspired approach to monocular depth estimation that leverages semantic cues, object size priors, and relational information to improve depth prediction accuracy, especially with limited training data.

Contribution

The work explicitly incorporates external semantic, size, and relational cues into depth estimation models, inspired by biological vision systems, to enhance performance and data efficiency.

Findings

Improved depth prediction accuracy on NYUD2 benchmark.

External semantic and size cues enhance model performance.

Method is adaptable to various depth estimation systems.

Abstract

Monocular depth estimation (MDE) aims to transform an RGB image of a scene into a pixelwise depth map from the same camera view. It is fundamentally ill-posed due to missing information: any single image can have been taken from many possible 3D scenes. Part of the MDE task is, therefore, to learn which visual cues in the image can be used for depth estimation, and how. With training data limited by cost of annotation or network capacity limited by computational power, this is challenging. In this work we demonstrate that explicitly injecting visual cue information into the model is beneficial for depth estimation. Following research into biological vision systems, we focus on semantic information and prior knowledge of object sizes and their relations, to emulate the biological cues of relative size, familiar size, and absolute size. We use state-of-the-art semantic and instance segmentation models to provide external information, and exploit language embeddings to encode relational information between classes. We also provide a prior on the average real-world size of objects. This external information overcomes the limitation in data availability, and ensures that the limited capacity of a given network is focused on known-helpful cues, therefore improving performance. We experimentally validate our hypothesis and evaluate the proposed model on the widely used NYUD2 indoor depth estimation benchmark. The results show improvements in depth prediction when the semantic information, size prior and instance size are explicitly provided along with the RGB images, and our method can be easily adapted to any depth estimation system.