16 citations · 41 across the 26 of their papers we have counts for
13 papers · 1 filter
Hydra++: Real-Time Hierarchical 3D Scene Graph Construction With Object-Level Shape Estimation
Hyungtae Lim, Nathan Hughes, Xihang Yu +5
3D scene graphs provide a hierarchical abstraction of environments by encoding spatial entities, such as objects and places, and their relationships. However, existing scene graph…
Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees
Harry Zhang, Nicolas Gorlo, Luca Carlone
Long-horizon robot operation requires spatio-temporal memory to record the environment state and recall it for downstream reasoning. Scene graphs and retrieval-augmented systems gr…
Understanding the Impact of Geometric Foundation Models on Vision-Language-Action Models
Yurou Yang, Muyuan Lin, Roberto Martin-Martin +4
Recent work explores new opportunities at the intersection of vision-language-action models (VLAs) and geometric foundation models (GFMs) for 3D reconstruction, such as VGGT. While…
VGGT-SLAM 2.0: Real-time Dense Feed-forward Scene Reconstruction
Dominic Maggio, Luca Carlone
We present VGGT-SLAM 2.0, a real-time RGB feed-forward SLAM system which substantially improves upon VGGT-SLAM for incrementally aligning submaps created from VGGT. Firstly, we rem…
Toward Zero-Shot Point Cloud Registration Across Diverse Scales, Scenes, and Sensor Setups
Hyungtae Lim, Minkyun Seo, Luca Carlone +1
Some deep learning-based point cloud registration methods struggle with zero-shot generalization, often requiring target-domain retraining, fine-tuning, or dataset-specific metric…
Describe Anything Anywhere At Any Moment
Nicolas Gorlo, Lukas Schmid, Luca Carlone
Computer vision and robotics applications ranging from augmented reality to robot autonomy in large-scale environments require spatio-temporal memory frameworks that capture both g…
Structured Interfaces for Automated Reasoning with 3D Scene Graphs
Aaron Ray, Jacob Arkin, Harel Biggie +3
In order to provide a robot with the ability to understand and react to a user's natural language inputs, the natural language must be connected to the robot's underlying represent…
Gaussian Mapping for Evolving Scenes
Vladimir Yugay, Thies Kersten, Luca Carlone +3
Mapping systems with novel view synthesis (NVS) capabilities, most notably 3D Gaussian Splatting (3DGS), are widely used in computer vision, as well as in various applications, inc…
VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold
Dominic Maggio, Hyungtae Lim, Luca Carlone
We present VGGT-SLAM, a dense RGB SLAM system constructed by incrementally and globally aligning submaps created from the feed-forward scene reconstruction approach VGGT using only…
Bayesian Fields: Task-driven Open-Set Semantic Gaussian Splatting
Dominic Maggio, Luca Carlone
Open-set semantic mapping requires (i) determining the correct granularity to represent the scene (e.g., how should objects be defined), and (ii) fusing semantic knowledge across m…
BUFFER-X: Towards Zero-Shot Point Cloud Registration in Diverse Scenes
Minkyun Seo, Hyungtae Lim, Kanghee Lee +2
Recent advances in deep learning-based point cloud registration have improved generalization, yet most methods still require retraining or manual parameter tuning for each new envi…
Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation
Mohsi Jawaid, Rajat Talak, Yasir Latif +2
Deep learning plays a critical role in vision-based satellite pose estimation. However, the scarcity of real data from the space environment means that deep models need to be train…
Mixed Diffusion for 3D Indoor Scene Synthesis
Siyi Hu, Diego Martin Arroyo, Stephanie Debats +3
Generating realistic 3D scenes is an area of growing interest in computer vision and robotics. However, creating high-quality, diverse synthetic 3D content often requires expert in…