Gabriel Perez S.

Papers

Total Citations

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About

Gabriel Perez S. is a researcher at the forefront of 3D computer vision, specializing in the robustness and certification of deep learning models for point cloud processing. His work addresses critical security concerns in high-stakes applications like autonomous driving and surgical robotics, where models must withstand real-world spatial deformations. His most notable contribution, **3DeformRS**, introduces a novel method for certifying the robustness of point cloud models against spatial deformations, providing formal guarantees that ensure reliability in safety-critical environments. This work, published in 2022, has garnered early recognition with 5 citations, reflecting its growing influence in the field. By bridging the gap between theoretical certification and practical deployment, Perez S. is shaping the future of trustworthy 3D perception systems. His research is essential reading for students and engineers working on robust computer vision, offering a rigorous framework to validate model behavior under real-world perturbations.