Enhancing self-supervised monocular depth estimation with traditional visual odometry (1908.03127v2)

Abstract: Estimating depth from a single image represents an attractive alternative to more traditional approaches leveraging multiple cameras. In this field, deep learning yielded outstanding results at the cost of needing large amounts of data labeled with precise depth measurements for training. An issue softened by self-supervised approaches leveraging monocular sequences or stereo pairs in place of expensive ground truth depth annotations. This paper enables to further improve monocular depth estimation by integrating into existing self-supervised networks a geometrical prior. Specifically, we propose a sparsity-invariant autoencoder able to process the output of conventional visual odometry algorithms working in synergy with depth-from-mono networks. Experimental results on the KITTI dataset show that by exploiting the geometrical prior, our proposal: i) outperforms existing approaches in the literature and ii) couples well with both compact and complex depth-from-mono architectures, allowing for its deployment on high-end GPUs as well as on embedded devices (e.g., NVIDIA Jetson TX2).

• The paper introduces an innovative method that integrates traditional visual odometry with a sparsity-invariant autoencoder to enrich depth cues.
• It demonstrates improved accuracy across key metrics on the KITTI dataset, highlighting benefits for autonomous navigation and mobile robotics.
• The framework maintains a self-supervised approach while enhancing existing monocular depth estimation architectures without external ground truth data.

Enhancing Self-Supervised Monocular Depth Estimation with Traditional Visual Odometry

The paper presents an innovative method for improving self-supervised monocular depth estimation by integrating traditional visual odometry (VO) algorithms. The core proposal is to supplement existing self-supervised deep learning frameworks with a geometrical prior derived from traditional VO, a move that addresses the deficiency of geometrical cues in conventional self-supervised monocular setups.

Methodology Overview

The approach leverages sparse three-dimensional data obtained from visual odometry algorithms as an additional input to a sparsity-invariant autoencoder, enhancing depth predictions from monocular images. The proposed framework consists of two primary components:

1. Sparsity-Invariant Autoencoder: This module processes sparse 3D measurements from VO, densifying them to produce richer depth cues. By maintaining the sparse nature of the inputs, the autoencoder effectively enriches the data provided to the main depth estimation network.
2. Depth Estimator: Operating with enhanced input—both RGB data and processed depth cues—the depth estimator is tasked with generating the final depth map. It is designed to be flexible, suitable for both compact and complex self-supervised architectures, such as PyD-Net and Monodepth.

The authors emphasize that this integration does not necessitate external ground truth data, thus maintaining the self-supervised nature of the depth estimation task.

Experimental Results

The experimental validation is conducted using the KITTI dataset, where the proposed framework demonstrates superior performance over existing self-supervised approaches. Notable improvements are observed in absolute and square relative error metrics, as well as RMSE and logarithmic RMSE, affirming the efficacy of incorporating VO-derived priors.

The method significantly enhances depth prediction accuracy across different scenarios, including autonomous navigation, where obtaining ground truth depth information is not feasible.

Practical and Theoretical Implications

Practically, the improved accuracy and flexibility of deploying the method on high-end GPUs and embedded devices underscore its potential for real-time applications such as mobile robotics and augmented reality. The framework's compatibility with existing architectures implies that it can serve as an upgrade path for enhancing the performance of current monocular depth estimation models.

Theoretically, this integration of traditional VO with deep learning challenges the community to reconsider the dichotomy between classical geometry-based methods and modern data-driven approaches. It opens avenues for new research that could explore other forms of traditional knowledge as priors in machine learning systems, potentially fostering hybrid models that combine the best of both realms.

Future Directions

Future developments might include extending this approach to unsupervised settings beyond stereo imagery, integrating additional sensor data for better scale estimation, or exploring its applicability to other domains where depth information is crucial. The approach sets a precedent for hybrid techniques, suggesting that a deeper synergy between traditional computer vision techniques and deep learning could yield substantial benefits for depth estimation and beyond.