3D Object Aided Self-Supervised Monocular Depth Estimation

TL;DR

This paper introduces a novel self-supervised monocular depth estimation method that leverages 3D object detection to improve accuracy by addressing dynamic scene elements and scale ambiguity.

Contribution

It proposes integrating monocular 3D object detection into depth estimation, enabling scale-aware, dynamic object handling and joint training for improved depth and object detection performance.

Findings

Achieves state-of-the-art depth estimation on KITTI dataset.

Joint training enhances both depth accuracy and 3D object detection.

Addresses scale ambiguity in monocular vision effectively.

Abstract

Monocular depth estimation has been actively studied in fields such as robot vision, autonomous driving, and 3D scene understanding. Given a sequence of color images, unsupervised learning methods based on the framework of Structure-From-Motion (SfM) simultaneously predict depth and camera relative pose. However, dynamically moving objects in the scene violate the static world assumption, resulting in inaccurate depths of dynamic objects. In this work, we propose a new method to address such dynamic object movements through monocular 3D object detection. Specifically, we first detect 3D objects in the images and build the per-pixel correspondence of the dynamic pixels with the detected object pose while leaving the static pixels corresponding to the rigid background to be modeled with camera motion. In this way, the depth of every pixel can be learned via a meaningful geometry model. Besides, objects are detected as cuboids with absolute scale, which is used to eliminate the scale ambiguity problem inherent in monocular vision. Experiments on the KITTI depth dataset show that our method achieves State-of-The-Art performance for depth estimation. Furthermore, joint training of depth, camera motion and object pose also improves monocular 3D object detection performance. To the best of our knowledge, this is the first work that allows a monocular 3D object detection network to be fine-tuned in a self-supervised manner.