Yuliang Guo

I'm a Lead (Staff) Research Scientist at Bosch AI Research Center at Silicon Valley, where I lead a team that works on 3D vision and interactive spatial AI solutions.

At Bosch, my work focuses on pioneering computer vision and AI research, aimed at enabling generalization across diverse hardware configurations and embodiments. My research has been integrated into multiple Bosch products or prototypes, such as assisted driving technologies, industrial augmented reality (AR) systems, and AI-powered indoor robotics.

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Research

My research focuses on developing active 3D perception methods adaptable across diverse embodiments and automating the creation of interactive digital twins of real-world environments. The ultimate goal is to enable intelligent human-AI interactions and advance physical AI for greater autonomy and adaptability.

Publications (show selected / show all by date)

(* indicates equal contribution, † indicates corresponding author or equal advising)

	Online Language Splatting Saimouli Katragadda, Cho-Ying Wu, Yuliang Guo^†, Xinyu Huang, Guoquan Huang, Liu Ren, arXiv 2025 [paper] [project page] [code] A fully online system to effectively integrate dense CLIP features with Gaussian Splatting. High-resolution dense CLIP embedding and online compressor learning modules are introduced to serve dense language mapping at realtime (40+ FPS) while retaining open-vocabulary capability for flexible query-based human-machine interaction.
	Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera Yuliang Guo^†, Sparsh Garg, S. Mahdi H. Miangoleh, Xinyu Huang, Liu Ren, CVPR 2025 [paper] [project page] [code] Depth Any Camera (DAC) is a powerful framework achieving superior zero-shot generalization in metric depth estimation for large FoV cameras, including fisheye and 360°. Tired of collecting new data for specific cameras? DAC maximizes the utility of every existing 3D data for training, regardless of the specific camera types used in new applications.
	SMART: Advancing Scalable Map Priors for Driving Topology Reasoning Junjie Ye, David Paz, Hengyuan Zhang, Yuliang Guo, Xinyu Huang, Henrik I. Christensen, Yue Wang, Liu Ren, ICRA 2025 [paper] [project page] [code] SMART augments online topology reasoning with robust map priors learned from scalable SD and satellite maps, substantially improving lane perception and topology reasoning.
	SUP-NeRF: A Streamlined Unification of Pose Estimation and NeRF for Monocular 3D Object Reconstruction Yuliang Guo, Abhinav Kumar, Cheng Zhao, Ruoyu Wang, Xinyu Huang, Liu Ren, ECCV 2024 [paper] [project page] [code] [talk] A monocular object reconstruction framework effectively integrating object pose estimation and NeRF-based reconstruction. A novel camera-invariant pose estimation module is introduced to resolve depth-scale ambiguity and enhance cross-domain generalization.
	TCLC-GS: Tightly Coupled LiDAR-Camera Gaussian Splatting for Autonomous Driving Cheng Zhao, Su Sun, Ruoyu Wang, Yuliang Guo, Jun-Jun Wan, Zhou Huang, Xinyu Huang, Yingjie Victor Chen, Liu Ren, ECCV 2024 [paper] [video demo] An advanced Gaussian Splatting method effectively fusing Lidar and surrounding camera views for autonomous driving. The method uniquely leverages an intermediate occ-tree feature volume before GS such that GS parameters can be initialized from feature-volume-generated 3D surface more effectively.
	Enhancing Online Road Network Perception and Reasoning with Standard Definition Maps Hengyuan Zhang, David Paz, Yuliang Guo, Arun Das, Xinyu Huang, Karsten Haug, Henrik I. Christensen, Liu Ren, IROS 2024 [paper] [project page] An effective framework leveraging lightweight and scalable priors-Standard Definition (SD) maps in the estimation of online vectorized HD map representations.
	SeaBird: Segmentation in Bird’s View with Dice Loss Improves Monocular 3D Detection of Large Objects Abhinav Kumar, Yuliang Guo^†, Xinyu Huang, Liu Ren, Xiaoming Liu^† CVPR 2024 [paper] [code] [project page] A mathematical framework to prove that the dice loss leads to superior noise-robustness and model convergence for large objects compared to regression losses. A flexible monocular 3D detection pipeline integrated with bird-eye view segmentation.
	Behind the Veil: Enhanced Indoor 3D Scene Reconstruction with Occluded Surfaces Completion Su Sun, Cheng Zhao, Yuliang Guo^†, Ruoyu Wang, Xinyu Huang, Victor(Yingjie) Chen, Liu Ren CVPR 2024 [paper] [project page] A neural reconstruction method enabling the completion the occluded surfaces from large 3D scene reconstrucion. A milestone in automating the creation of interactable digital twins from real world.
	3D Copy-Paste: Physically-Plausible Object Insertion for Monocular 3D Detection Yuhao Ge, Hong-Xing Yu, Cheng Zhao, Yuliang Guo, Xinyu Huang, Liu Ren, Laurent Itti, Jiajun Wu NeurIPS 2023 [paper] [code] [project page] A physically plausible indoor 3D object insertion approach to automatically "copy" virtual objects and "paste" them into real scenes.
	Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation Nathaniel Merrill, Yuliang Guo^†, Xingxing Zuo, Xinyu Huang, Stefan Leutenegger, Xi Peng, Liu Ren, Guoquan Huang^† CVPR 2022 [paper] [code] A keypoint-based object-level SLAM framework that can provide globally consistent 6DoF pose estimates for symmetric and asymmetric objects.
	OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion Yuyan Li, Yuliang Guo, Zhixin Yan, Xinyu Huang, Ye Duan, Liu Ren, CVPR 2022 (Oral Presentation)** [paper] [code] The first vision transformer approach to handle 360 monocular depth estimation with spherical distortion. Novel designs include tangent-image coordinate embedding and geometry-aware feature fusion.
	PoP-Net: Pose over Parts Network for Multi-Person 3D Pose Estimation from a Depth Image Yuliang Guo, Zhong Li, Zekun Li, Xiangyu Du, Shuxue Quan, Yi Xu, WACV 2022 [paper] [code] [dataset] A real-time method to predict multi-person 3D poses from a depth image. Introduce new part-level representation to enables an explicit fusion process of bottom-up part detection and global pose detection. A new 3D human posture dataset with challenging multi-person occlusion.
	Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection Yuliang Guo, GuanGuang Chen, Peitao Zhao, Weide Zhang, Jinghao Miao, Jingao Wang, Tae Eun Choe ECCV 2020 [paper] [code] [dataset] A pioneer work in predicting 3D lanes from a single image with high generalization to novel scenes. A 3D lane synthetic dataset is introduced.
	Differential Geometry in Edge Detection: Accurate Estimation of Position, Orientation and Curvature Benjamin B. Kimia, Xiaoyan Li, Yuliang Guo, Amir Tamrakar TPAMI 2018* [paper] [code] [dataset] Numerically robust image filters and symbolic curve models are introduced to precisely estimate differential geometry attributes associated with image edges, including localization, orientation, curvature, edge topology.
	Robust Pose Tracking with a Joint Model of Appearance and Shape Yuliang Guo, Lakshmi N. Govindarajan, Benjamin B. Kimia, Thomas Serre arXiv 2018 [paper] A joint model of learned part-based appearance and parametric shape representation to precisely estimate the highly articulated poses of multiple laboratory animals.
	BoMW: Bag of manifold words for one-shot learning gesture recognition from kinect Lei Zhang, Shengping Zhang, Feng Jiang, Yuankai Qi, Jun Zhang, Yuliang Guo, Huiyu Zhou IEEE Transactions on Circuits and Systems for Video Technology 2017 [Paper] One-shot learning gesture recognition on RGB-D data recorded from Microsoft Kinect. A novel bag of manifold words (BoMW) based feature representation on sysmetric positive definite (SPD) manifolds.
	A Systematic Comparison between Visual Cues for Boundary Detection David A. Mely, Junkyung Kim, Mason McGill, Yuliang Guo, Thomas Serre Vision Research 2016 [paper] [dataset] This study investigates the relative diagnosticity and the optimal combination of multiple cues (we consider luminance, color, motion and binocular disparity) for boundary detection in natural scenes. A multi-cue boundary dataset is introduced to facilitate the study.
	A Multi-Stage Approach to Curve Extraction Yuliang Guo, Naman Kumar, Maruthi Narayanan, Benjamin B Kimia ECCV 2014 [paper] [code] A multi-stage approach to curve extraction where the curve fragment search space is iteratively reduced by removing unlikely candidates using geometric constrains, but without affecting recall, to a point where the application of an objective functional becomes appropriate.

Selected Patents

Yuliang Guo, Xinyu Huang, Liu Ren, Systems and methods for providing product assembly step recognition using augmented reality, US Patent 11,715,300, 2023

Yuliang Guo, Xinyu Huang, Liu Ren, Semantic SLAM Framework for Improved Object Pose Estimation, US Patent App. 17/686,677, 2023

Yuliang Guo, Zhixin Yan, Yuyan Li, Xinyu Huang, Liu Ren, Method for fast domain adaptation from perspective projection image domain to omnidirectional image domain in machine perception tasks, US Patent App. 17/545,673, 2023

Yuliang Guo, Tae Eun Choe, KaWai Tsoi, Guang Chen, Weide Zhang, Determining vanishing points based on lane lines, US Patent 11,227,167, 2022

Tae Eun Choe, Yuliang Guo, Guang Chen, KaWai Tsoi, Weide Zhang, Sensor calibration system for autonomous driving vehicles, US Patent 10,891,747, 2021