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About

Vitor Guizilini is a prominent researcher whose work spans autonomous robotics, computer vision, and 3D scene understanding, with particular expertise in self-supervised depth estimation and multi-camera perception systems. He is perhaps best known for developing PackNet, a groundbreaking self-supervised monocular depth estimation framework that learns rich 3D representations directly from unlabeled video, earning 35 citations and inspiring a wave of follow-on research. His influential contributions extend to full surround-view depth estimation from multiple cameras (51 citations) and joint learning of optical flow, depth, and scene flow without real-world labels (54 citations), collectively advancing the viability of camera-only perception as an affordable alternative to LiDAR in autonomous driving. Guizilini has also made significant strides in self-supervised camera calibration, viewpoint-equivariant 3D object detection, and large-scale occupancy mapping using Hilbert Maps. Earlier in his career, he contributed foundational work in visual odometry and UAV security, including an eye-opening study on denial-of-service vulnerabilities in commercial drones (64 citations). Across more than a decade of research, Guizilini has consistently pushed the boundaries of scalable, label-efficient robot perception, making his work essential reading for anyone working at the intersection of deep learning and autonomous systems.