Ning Xu

Senior Machine Learning Engineer at NVIDIA.

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I am a Senior Machine Learning Engineer at NVIDIA, working on world simulation for autonomous vehicles. Over the past six years, I have built spatial systems across the autonomy stack, spanning centimeter-accurate localization, online mapping, city-scale 3D SLAM, and neural scene reconstruction. This progression now drives a broader question in my work: how can we build models that not only reconstruct environments but also simulate how they evolve through interaction?

At NVIDIA, I helped bring Instant NuRec — a feed-forward 3D Gaussian Splatting model that turns a short multi-camera driving log into a fully simulatable 3D world — into NVIDIA’s autonomous vehicle simulation stack. Previously, at Nuro, I developed BEV-transformer models for online HD mapping and 3D SLAM systems operating at multi-city scale. At Motional, I engineered centimeter-accurate localization for production robotaxis. I received my M.S. in Robotics from the University of Michigan, advised by Prof. Chad Jenkins, and my B.E. from Beihang University.

I am particularly interested in action-conditioned world models, 3D/4D generative reconstruction, and data-driven simulation. I believe world models will be key to creating diverse, controllable, and closed-loop simulation environments for training and evaluating robots and autonomous vehicles at scale.

Selected Publications

  1. arXiv
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    Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation
    Jiahui Huang, Jiawei Ren, Michal Tyszkiewicz, Bjoern Haefner, Michael Shelley, Xin Kang, Seung Wook Kim, Ning Xu, Qi Wu, Janick Martinez Esturo, Shengyu Huang, Nick Schneider, Laura Leal-Taixé, Zan Gojcic, and Sanja Fidler
    arXiv preprint arXiv:2607.14203, 2026
    arXiv Bib Code Website
  2. CVPR
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    Exploring Real World Map Change Generalization of Prior-Informed HD Map Prediction Models
    Samuel M. Bateman, Ning Xu, H. Charles Zhao, Yael Ben Shalom, Vince Gong, Greg Long, and Will Maddern
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Autonomous Driving, 2024
    arXiv Bib
  3. RA-L
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    GeoFusion: Geometric Consistency informed Scene Estimation in Dense Clutter
    Zhiqiang Sui, Haonan Chang, Ning Xu, and Odest Chadwicke Jenkins
    IEEE Robotics and Automation Letters, 2020
    arXiv Bib

Selected Projects

NVIDIA neural reconstruction for autonomous vehicles
Neural Reconstruction for Autonomous Vehicles at NVIDIA
Turning real-world driving logs into fully simulatable 3D Gaussian Splatting worlds for closed-loop AV simulation with NVIDIA Omniverse NuRec — from integrating the feed-forward Instant NuRec model into the production reconstruction pipeline to delivering the camera-only NuRec solution.
Video
Nuro unified perception model
Unified Perception Model at Nuro
Prototyped and developed a BEV-transformer model for online HD map construction, integrated within Nuro's unified camera–LiDAR BEV perception framework — accelerating the deployment of multi-task perception models onto the road.
Blog
Nuro scalable online mapping
Scalable Online Mapping at Nuro
Applied research on fusing prior HD map data with real-time sensor streams, significantly improving robustness and safety against real-world environmental and structural map changes — the foundation of our CVPR 2024 paper.
Blog
Nuro 3D city-scale SLAM
3D City-Scale SLAM System at Nuro
Built and maintained a highly reliable, multi-city-scale 3D mapping and SLAM pipeline — analyzing and optimizing scan matching and parallel graph optimization to unlock massive-scale, physics-grounded map building.
Blog