Scalable Contextualized Scene Representation for Robotics

We are building a framework for large-scale, semantically-aware Gaussian splats to be used in robotic planning. By leveraging scene graphs and shared Gaussian embeddings, our system allows for dynamic, task-driven Level-of-Detail filtering and efficient querying by Vision-Language Models. This approach will be deployed on an ANYmal legged robot in challenging outdoor environments, with a XR pilot interacting with the semantic scene.

Project Team

Prof. Marco Hutter

ETH Zurich

Dr. Vaishakh Patil

ETH Zurich

Dr. Keisuke Tateno

Google

PD Dr. Federico Tombari

Google

Maximum Wilder-Smith

ETH Zurich

Publications

ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation

Authors:

Arjun Bhardwaj

Maximum Wilder-Smith

Mayank Mittal

Dr. Vaishakh Patil

Prof. Marco Hutter

Published:

Proceedings of Robotics: Science and Systems, 2026

MOSAIC-GS: Monocular Scene Reconstruction via Advanced Initialization for Complex Dynamic Environments

Authors:

Svitlana Morkva

Maximum Wilder-Smith

Michael Oechsle

Alessio Tonioni

Prof. Marco Hutter

Dr. Vaishakh Patil

Published:

Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2026

DiskChunGS: Large-Scale 3D Gaussian SLAM Through Chunk-Based Memory Management

Authors:

Casimir Feldmann

Maximum Wilder-Smith

Dr. Vaishakh Patil

Michael Oechsle

Michael Niemeyer

Dr. Keisuke Tateno

Prof. Marco Hutter

Published:

IEEE Robotics and Automation Letters, 2026