The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation (2307.15061v2)

Abstract: Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical applications. Existing depth estimation systems, however, suffer inevitably from real-world corruptions and perturbations and are struggled to provide reliable depth predictions under such cases. In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation. This challenge was developed based on the newly established KITTI-C and NYUDepth2-C benchmarks. We hosted two stand-alone tracks, with an emphasis on robust self-supervised and robust fully-supervised depth estimation, respectively. Out of more than two hundred participants, nine unique and top-performing solutions have appeared, with novel designs ranging from the following aspects: spatial- and frequency-domain augmentations, masked image modeling, image restoration and super-resolution, adversarial training, diffusion-based noise suppression, vision-language pre-training, learned model ensembling, and hierarchical feature enhancement. Extensive experimental analyses along with insightful observations are drawn to better understand the rationale behind each design. We hope this challenge could lay a solid foundation for future research on robust and reliable depth estimation and beyond. The datasets, competition toolkit, workshop recordings, and source code from the winning teams are publicly available on the challenge website.

• The paper introduces the RoboDepth Challenge, a competition designed to enhance depth estimation robustness under out-of-distribution scenarios.
• It outlines innovative methods including spatial- and frequency-domain augmentations, masked image modeling, and vision-language pre-training.
• The challenge engaged over 200 participants and demonstrated significant improvements, impacting safety-critical applications like autonomous vehicles and AR.

The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation

The paper "The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation" examines the development and outcomes of a competition initiated to advance depth estimation under out-of-distribution (OoD) scenarios. This topic is particularly significant for safety-critical applications, as accurate depth estimation systems should reliably function under various real-world corruptions, such as adverse weather, noise, and sensor perturbations.

The competition was organized as part of the IEEE ICRA 2023 Conference to address the vulnerability of current depth estimation models to common real-world corruptions. Participants utilized the KITTI-C and NYUDepth2-C datasets as benchmarks for evaluating their solutions. The challenge consisted of two tracks: robust self-supervised and robust fully-supervised depth estimation. Methods proposed in these tracks covered multiple innovative strategies aimed at improving robustness.

Key approaches highlighted in the paper include:

• Spatial- and Frequency-Domain Augmentations: These techniques explore data augmentation strategies that modify both spatial and frequency characteristics of input data. They are designed to improve a model's robustness by training it to handle various corruptions.
• Masked Image Modeling and Restoration: The use of masking-based image reconstruction and restoration techniques, such as diffusion-based noise suppression, showed potential in enhancing OoD robustness, leveraging methods pioneered in unsupervised learning paradigms.
• Vision-Language Pre-training: Leveraging pre-trained text features from models such as CLIP and aligning them with visual features can significantly improve the model’s performance on corrupted datasets.
• Adversarial Training and Hierarchical Feature Enhancement: These strategies emphasize robustness by training models to combat adversarial perturbations and enhance representation through hierarchical structures.

Remarkably, the competition attracted over two hundred participants, yielding substantial engagement in evaluating model performance under the proposed benchmarks. The top-performing solutions demonstrated significant improvements over baselines, utilizing advanced data augmentation, model ensemble strategies, and novel architecture designs.

The implications of this research are broadly relevant to fields that require reliable depth estimation, such as autonomous vehicles, augmented reality, and robotics. Methodologies developed here contribute to advancing safety-critical applications by enhancing the generalization capabilities of depth estimation algorithms under unexpected conditions.

Looking forward, further efforts could extend the dataset scale and diversity to cover more real-world scenarios, incorporate more complex depth estimation tasks, and strive for a balance between robustness and computational efficiency. Such advancements would be invaluable for the practical deployment of reliable depth estimation technologies.