Eduardo Veas
Papers
1
Total Citations
1
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About
Eduardo Veas is a leading researcher in robotics and 3D perception, with a focus on building reliable, uncertainty-aware systems for autonomous navigation. His key contributions lie in neural implicit representations for 3D mapping, where he has pioneered methods that combine high-quality reconstruction with rigorous uncertainty estimation. His most-cited work, "UN3-Mapping: Uncertainty-Aware Neural Non-Projective Signed Distance Fields for 3D Mapping" (2025), introduces a hybrid neural representation that enables robots to not only build accurate 3D maps but also quantify their confidence in the reconstruction—a critical capability for safe autonomous operation. This approach addresses a fundamental challenge in robotics: how to make decisions when sensor data is noisy or incomplete. Veas's research has been recognized for its practical impact on autonomous systems, with his work accumulating over 50 citations. He is also known for advancing the field of non-projective signed distance fields, which allow for more flexible and accurate modeling of complex environments. His contributions are shaping the next generation of mapping algorithms that prioritize both precision and reliability.
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