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Live+20 todayUpdated 1h ago

3D Vision Pioneer Andrea Tagliasacchi joins Wayve Labs as Principal Scientist

Andrea Tagliasacchi, a leading researcher in 3D deep learning, joins Wayve Labs to advance embodied AI for autonomy.

Citations

over 15,000

Research papers

more than 100

Patents

nearly two dozen

What Happened

Wayve has appointed Andrea Tagliasacchi as Principal Scientist at Wayve Labs in Vancouver. He will lead foundational research on 3D scene understanding and neural scene representations to advance embodied AI for autonomy. Tagliasacchi brings extensive experience from his time at Google and academia, with over 15,000 citations and nearly two dozen patents.

Andrea is one of the most respected researchers at the boundary of 3D vision and learned scene representations, and I’m excited to have him join Wayve Labs. The problems he’s spent his career on, understanding and reconstructing the world from images, sit right at the heart of what we’re building with embodied AI.
Jamie Shotton, Chief Scientist, Wayve
Key Contributions
  • Neural Radiance Fields (MobileNeRF)
  • Gaussian Splatting (3DGS-MCMC, pixelSplat)
  • Generative 3D works (Scene Representation Transformers, 3DiM, 360Anything)
  • Kubric synthetic dataset

Why this matters

This hire bolsters Wayve's foundational research in 3D world models and neural rendering, essential for developing autonomous systems that understand complex environments.

Terms in This Story

Neural Radiance Fields (NeRF)
A technique that uses neural networks to generate 3D scenes from 2D images by learning the color and density of each point in space.
Gaussian Splatting
A real-time 3D rendering method that represents scenes as collections of Gaussian primitives.
Embodied AI
AI systems that can perceive, reason, and act within physical environments, such as autonomous vehicles.
Novel view synthesis
The task of generating new viewpoints of a scene from a set of existing images.
Read Original: Wayve Technologies Ltd.

Summarised from the linked release; details can be imperfect — always verify against the original source.