Speaker	Talk	YouTube link
	Paul-Edouard Sarlin Senior Research Scientist Google	Geometry is the harness of 3D learning Thu, Oct 8, 2026 · 11:00 AM ET Abstract End-to-end learning models for 3D reconstruction offer speed and simplicity, yet they struggle with high-precision tasks and rely on classical systems for their training data. This talk addresses a central debate in the field: are classical SLAM pipelines becoming obsolete, or are they evolving into the essential foundation for neural representations? Rather than abandoning explicit mapping, we argue that classical geometry must serve as the "harness" for prior-rich deep learning models. By mathematically fusing probabilistic state estimation with learned priors, hybrid optimization systems can achieve state-of-the-art robustness and accuracy. We also challenge the pervasive myth that geometric optimization is inherently too slow by demonstrating highly optimized modern bundle adjustment techniques. Ultimately, much like Large Language Models utilizing external calculators for precise arithmetic, next-generation 3D foundation models must leverage explicit geometric optimization as a dedicated tool, allowing neural networks to focus their capacity on robustness and learned representations.