Papers
4
Total Citations
40
H-Index
3
About
Yifu Tao is an emerging robotics and computer vision researcher whose work sits at the intersection of 3D reconstruction, neural radiance fields (NeRF), and autonomous navigation. His research focuses on developing scalable, high-fidelity environment representations that enable robots to perceive and navigate complex real-world spaces with precision and reliability. Tao's most significant contribution is the SiLVR system — a large-scale lidar-visual reconstruction framework that fuses lidar depth data with camera imagery using Neural Radiance Fields. First introduced in 2024 (24 citations) and extended in 2025 to incorporate uncertainty quantification, SiLVR produces geometrically accurate, photorealistic reconstructions particularly suited for robotic inspection tasks. This line of work represents a meaningful advance in bridging classical sensor fusion with modern neural scene representations. His 2022 work on probabilistic depth completion for 3D lidar reconstruction (11 citations) addresses the practical challenge of sparse lidar measurements in motion planning, demonstrating his commitment to safety-critical robotic applications. More recently, his comparative study of point cloud, mesh, and NeRF representations for visual localization further underscores his broad expertise in 3D map-based navigation. With a growing citation record, Tao is establishing himself as a promising contributor to next-generation robotic perception systems.
Research Focus
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Top Papers
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