Description:

Mono KITTI is a specialized version of the KITTI dataset that focuses exclusively on monocular images and the corresponding distance measurements. This dataset is designed to facilitate research and development in the field of monocular absolute distance estimation, providing a rich set of data for training and evaluating machine learning models.

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Key Features:

Monocular Images: The dataset consists solely of images captured from a single camera, making it ideal for tasks that require monocular vision.

Distance Measurements: Each image is paired with accurate distance measurements, enabling precise absolute distance estimation.

High-Quality Data: Derived from the renowned KITTI dataset, Mono KITTI maintains high standards of image quality and accuracy.

Diverse Environments: The dataset includes a variety of scenes, from urban to rural environments, offering a comprehensive set of conditions for model training.

Dataset Composition:

Image Data: High-resolution monocular images covering diverse driving scenarios.

Distance Annotations: Accurate distance labels provided for each image, essential for absolute distance estimation tasks.

Training, Validation, and Test Sets: The data is split into well-defined training, validation, and test sets to support robust model development and evaluation.

Applications:

Monocular Distance Estimation: Ideal for developing and testing algorithms that estimate distances from monocular images.

Autonomous Driving: Supports research in autonomous driving by providing data for critical perception tasks.

Computer Vision Research: A valuable resource for advancing the state-of-the-art in monocular vision and distance estimation.

Methodology:

The distance annotations in Mono KITTI are derived using advanced techniques to ensure high accuracy. This involves leveraging stereo vision data from the original KITTI dataset to extract precise distance measurements, which are then paired with the corresponding monocular images.

This dataset is sourced from Kaggle.