Ankit Dhiman

Papers

Total Citations

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About

Ankit Dhiman’s research centers on computer vision and deep learning, with a particular focus on depth completion—a critical task for enabling accurate 3D reconstruction, mixed reality, and robotic perception. His most cited work, “DeepDNet: Deep Dense Network for Depth Completion Task” (2021, 12 citations), introduces a novel deep dense network that transforms sparse depth data and captured views into dense, high-quality depth maps. This contribution addresses a fundamental challenge in scene understanding, where existing methods often struggle with incomplete or noisy depth information. By designing a network architecture that effectively fuses sparse inputs with visual cues, Dhiman’s approach enhances the reliability of depth estimation for real-world applications. His work demonstrates a clear impact in the field, providing a practical solution for systems that demand precise spatial awareness. Through DeepDNet and related research, Dhiman has established himself as a contributor to advancing depth completion techniques, with his findings serving as a reference for subsequent studies in autonomous navigation, augmented reality, and 3D modeling. His efforts highlight the ongoing push toward more robust and efficient vision systems.