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Unsupervised Training for Metric Monocular Road-Scene Depth Estimation


Core Concepts
StableCamH enables unsupervised training of monocular depth networks to learn absolute scale and metric accuracy using object size priors.
Abstract
Introduction Monocular depth estimation is crucial for autonomous driving and ADAS. Supervised methods are accurate but costly in data collection. Self-supervision Recent methods leverage self-supervision to avoid costly supervision. Scale ambiguity remains a challenge in self-supervised methods. Weak Supervision Various weak supervision methods rely on auxiliary sensors for scale awareness. Object Size Priors Leveraging object sizes from road scenes can inform metric scale. StableCamH Framework StableCamH aggregates scale information from object sizes into camera height estimates. Experiments Extensive experiments on KITTI and Cityscapes datasets show the effectiveness of StableCamH. Related Work Comparison with other self-supervised and weakly supervised methods.
Stats
StableCamH detects and estimates the sizes of cars in the frame. Extensive experiments on KITTI and Cityscapes datasets show the effectiveness of StableCamH.
Quotes
"Simply learning from an object size prior would, however, be too brittle since the metric supervision will be as ambiguous as the accuracy of that prior." "We humans not only possess rough prior knowledge about the vehicle size but can also estimate it more accurately by extracting instance-specific information such as car models from its appearance."

Key Insights Distilled From

by Genki Kinosh... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2312.04530.pdf
Camera Height Doesn't Change

Deeper Inquiries

How does StableCamH address the challenges posed by scale ambiguity in self-supervised methods

StableCamH addresses the challenges posed by scale ambiguity in self-supervised methods by leveraging a novel training framework that incorporates object size priors to learn metric scale. Traditional self-supervised methods often struggle with scale ambiguity, leading to inaccurate depth estimations. StableCamH overcomes this issue by aggregating scale information from known object sizes, such as cars on the road, into camera height estimates. By enforcing consistency in camera height across frames and epochs, StableCamH provides robust supervision for learning absolute scale without the need for auxiliary sensors or manual annotations. This approach ensures that monocular depth networks trained with StableCamH become not only scale-aware but also metric-accurate.

What are the implications of training a model on mixed datasets with different camera heights

Training a model on mixed datasets with different camera heights has significant implications for enhancing generalizability and improving performance in various scenarios. By enabling models to learn from diverse datasets captured at different camera heights, StableCamH can adapt to varying real-world conditions more effectively. This leads to higher generalization capabilities and better performance when deployed in practical applications where data may come from multiple sources with varying camera setups. Additionally, training on mixed datasets allows for broader coverage of scenarios and environments, making the model more versatile and robust.

How can leveraging object size priors improve monocular depth estimation beyond traditional approaches

Leveraging object size priors can improve monocular depth estimation beyond traditional approaches by providing valuable prior knowledge about known objects' dimensions in a scene. These priors offer additional constraints that help guide the depth estimation process towards more accurate results. By incorporating learned size priors like LSP (Learned Size Prior), models can estimate object dimensions based on appearance features rather than relying solely on pixel-level information from images. This approach enhances accuracy and robustness in estimating depths of objects like cars which are common elements in road scenes.
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