Justin L. Vincent

Papers

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About

Justin L. Vincent is a researcher at the forefront of depth sensing and perception for autonomous systems, with a primary focus on enabling robust navigation and manipulation in robotics. His most notable contribution is the development of **OnboardDepth**, a pioneering learning-based system that predicts accurate scene depth by leveraging a hybrid of supervised and unsupervised sensor supervision. This work, published in 2019, addresses a critical bottleneck in real-world robotics—how to achieve reliable depth estimation without relying solely on expensive, high-fidelity sensors. While still an emerging contribution with 2 citations, the methodology introduced in OnboardDepth has laid important groundwork for cost-effective, onboard perception pipelines. Vincent’s research bridges the gap between theoretical computer vision and practical deployment, offering scalable solutions for robots operating in unstructured environments. His work is particularly relevant for students and engineers seeking efficient depth prediction methods that can adapt to diverse sensor inputs, making him a key voice in the evolution of autonomous perception.