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            <h1 class="title is-1 publication-title">GET-3D</h1>
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                  <a href="https://scholar.google.co.in/citations?user=dRjWRJcAAAAJ&hl=en" target="_blank">Abhishek Mukhopadhyay</a></sup>,</span>
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                    <a href="https://scholar.google.com/citations?user=4-55tyYAAAAJ&hl=en" target="_blank">Anujith Muraleedharan</a></sup>,</span>
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                      <a href="https://scholar.google.com/citations?user=_UoINqgAAAAJ&hl=en" target="_blank">Mukund Mitra</a></sup>,</span>
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                        <a href="https://scholar.google.co.in/citations?user=Pyj57Y4AAAAJ&hl=en" target="_blank">Pradipta Biswas</a>
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                    <span class="author-block">Indian Institute of Science, Bengaluru<br>AVIATION Journal Vol 27 No 4 (2023)</span>

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            This project focuses on enhancing 2D videos to create a 3D-like immersive experience using a combination of deep learning-based instance segmentation and depth estimation techniques. By leveraging the YOLOv8 instance segmentation model and MiDaS depth estimation model, the system accurately identifies foreground objects and estimates their spatial depth. The goal is to apply dynamic depth-based effects that amplify the perception of depth, making the video content appear more engaging and visually striking without the need for specialized 3D glasses.
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            The methodology involves a multi-stage pipeline that integrates object segmentation, depth estimation, and depth-based effect application. Initially, the YOLOv8 instance segmentation model detects and segments objects of interest in each video frame. Simultaneously, the MiDaS model estimates the depth map of the scene, providing pixel-wise depth information. The segmented objects are then analyzed based on their depth and area to determine their eligibility for the 3D enhancement effect. Objects meeting specific depth thresholds are isolated, and zoom effects are applied to simulate depth perception.

            To further accentuate the 3D illusion, black vertical bars are added to the background, along with masked horizontal sections at the top and bottom of the frame. These bars serve two purposes: they create a frame-like boundary that enhances the viewer's focus on the foreground objects, simulating a window effect commonly seen in stereoscopic displays, and they reduce peripheral distractions, making the depth cues more prominent. Additionally, geometric transformations, such as resizing and perspective adjustments, are utilized to enhance the illusion of depth. The processed frames are compiled to generate a final video that presents an autostereoscopic 3D-like effect.
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            This work presents an end-to-end solution using state-of-the-art components for real-world aircraft navigation, contrasting with existing systems that operate in simulated environments. Traditionally, manual inspection is needed after landing before an aircraft follows the taxiway. Our approach integrates lane and object detection algorithms to automate this process, ensuring collision avoidance and precise stopping. The navigation algorithm controls steering and halts the aircraft if the taxiway ends or the lane is undetected.
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            The primary objective revolved around comparing the controllers' ability to effectively correct the Turtlebot's deviation from the central lane. To achieve this, we designed three distinct paths – left, right, and central – and systematically examined the performance of all four controllers across these paths. The findings indicated that the Linear Quadratic Regulator (LQR) exhibited the highest accuracy, with a mean error of 0.26 cm and a standard deviation of 0.94.
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      <pre><code>@article{Gaikwad2023,
        title={Developing a computer vision based system for autonomous taxiing of aircraft},
        author={Gaikwad, P. and Mukhopadhyay, A. and Muraleedharan, A. and Mitra, M. and Biswas, P.},
        journal={Aviation},
        volume={27},
        number={4},
        pages={248--258},
        year={2023},
        month={Dec},
        doi={10.3846/aviation.2023.20588}
        }</code></pre>
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