Taha Samavati

Iran University of Science and Technology

Papers

Total Citations

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About

Taha Samavati is a researcher at the forefront of computer vision and deep learning, with a particular focus on 3D reconstruction and scene understanding. His most-cited work, the comprehensive survey "Deep learning-based 3D reconstruction: a survey" (2023), has already garnered 84 citations, reflecting its timely and foundational impact on the field. This survey systematically maps the rapidly evolving landscape of deep learning techniques for inferring 3D structure from 2D images, establishing a critical reference for both newcomers and experts. Samavati’s contributions extend beyond surveys to novel methodologies that bridge the gap between traditional geometric approaches and modern learning-based paradigms, enabling more accurate and efficient 3D modeling from sparse or noisy data. His work is particularly influential in applications such as autonomous navigation, augmented reality, and medical imaging, where robust 3D understanding is paramount. Recognized for his ability to synthesize complex technical domains, Samavati’s research continues to shape how machines perceive and reconstruct the three-dimensional world, making him a rising voice in the intersection of deep learning and geometric computer vision.