Improving Depth Edge Localization in Sparsely-Supervised Monocular Depth Estimation

Core Concepts

A method to improve the localization of depth edges in sparsely-supervised monocular depth estimation models, while preserving per-pixel depth accuracy.

Abstract

The paper proposes a method to improve the localization of depth edges in sparsely-supervised monocular depth estimation (MDE) models. The key insights are:

MDE models tend to focus more on estimating continuous depth surfaces rather than accurate depth edges, due to the small impact of depth edges on the network's loss and alignment errors between RGB and LIDAR data.

To address this, the authors train a Depth Edge Estimation (DEE) network on synthetic data with dense depth ground truth to predict depth edge probability maps. These are then used to supervise the depth edges during training of the MDE model.

To evaluate the depth edge quality, the authors introduce new depth edge evaluation sets for the KITTI and DDAD datasets by manually annotating depth edges in a subset of images.

Experiments show that the proposed method significantly improves the depth edge localization accuracy on these evaluation sets, while maintaining comparable per-pixel depth accuracy to state-of-the-art MDE baselines.

The method is demonstrated to improve the realism of virtual object occlusions in augmented reality applications.

Stats

The LIDAR measurements in KITTI and DDAD datasets are sparse near depth edges, making the standard depth accuracy metric a poor measure of depth edge quality. (Fig. 3)
The authors manually annotated 102 images from KITTI and 50 images from DDAD with depth edge ground truth to enable direct evaluation of depth edge accuracy. (Sec. 4.1)

Quotes

"Significant errors are typically found in the proximity of depth discontinuities, i.e., depth edges, which often hinder the performance of depth-dependent applications that are sensitive to such inaccuracies, e.g., novel view synthesis and augmented reality."
"To the best of our knowledge this paper is the first attempt to address the depth edges issue for LIDAR-supervised scenes."

Key Insights Distilled From

Mind The Edge

by Lior Talker,... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2212.05315.pdf

Deeper Inquiries

How could the proposed method be extended to handle dynamic scenes with moving objects

To handle dynamic scenes with moving objects, the proposed method could be extended by incorporating motion estimation techniques. By integrating optical flow or other motion estimation algorithms, the DEE network could predict depth edges that account for the movement of objects in the scene. This would allow the MDE model to adapt to changes in the environment caused by moving objects and produce more accurate depth estimations around dynamic elements.

What other applications beyond augmented reality could benefit from improved depth edge localization

Beyond augmented reality, improved depth edge localization could benefit applications such as autonomous driving, robotics, and 3D reconstruction. In autonomous driving, accurate depth edges are crucial for obstacle detection and collision avoidance. Robotics applications could use precise depth edges for object manipulation and navigation. Additionally, in 3D reconstruction tasks, better depth edge localization can enhance the quality of reconstructed scenes and objects.

How could the DEE network training on synthetic data be further improved to better generalize to real-world scenes

To enhance the generalization of the DEE network trained on synthetic data to real-world scenes, several improvements can be implemented:

Domain Adaptation Techniques: Utilize domain adaptation methods to bridge the gap between synthetic and real data distributions. Techniques like adversarial training or domain adversarial neural networks can help the DEE network better adapt to real-world scenarios.
Data Augmentation: Increase the diversity of synthetic data by incorporating various lighting conditions, textures, and object configurations. This can help the DEE network learn a more robust representation of depth edges.
Semi-Supervised Learning: Incorporate a semi-supervised learning approach where the DEE network is trained on a combination of synthetic and partially labeled real data. This can help the network learn from limited real-world annotations while leveraging the rich information in synthetic data.
Transfer Learning: Pre-train the DEE network on a related task with abundant real-world data before fine-tuning on the synthetic dataset. This transfer learning approach can help the network capture more relevant features for depth edge estimation in real-world scenes.