16 citations · 41 across the 26 of their papers we have counts for
11 papers · 1 filter
3D Scene Graph Prediction: Generating Hierarchical Models from Partially Observed Environments
Siyi Hu, Jared Strader, Hyungtae Lim +1
Generating realistic 3D indoor scenes is an area of growing interest in computer vision and robotics. Existing methods, often motivated by applications such as interior design, gen…
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…
3D Scene Graphs: Open Challenges and Future Directions
Dennis Rotondi, Francesco Argenziano, Sebastian Koch +10
3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relational abstractions of the environment. Thei…
FOUND-IT: Foundation-model-first Task-driven 3D Scene Graphs with Granularity on Demand
Dominic Maggio, Nicolas Gorlo, Kris Hauser +1
We present the first approach to build hierarchical task-driven 3D scene graphs of arbitrary indoor or outdoor environments using an uncalibrated monocular camera in real-time. We…
Language as a Sensor: Calibrated Spatial Belief Estimation in 3D Scenes from Natural Language
Aryan Naveen, Jason Xinyu Liu, Luca Carlone +1
Robots deployed in human-centric environments routinely receive natural-language descriptions of spatial information ("I left my backpack on the table") that reference parts of the…
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…
Worth Remembering: Surprise-Gated Robot Episodic Memory
Nicolas Gorlo, Derek K. Wise, Alberto Speranzon +1
Robots solving generalist tasks need to be able to ground instructions in their past experience, since humans may refer to notable past events when giving a task (e.g., ``Take me t…
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…
Pandora: Articulated 3D Scene Graphs from Egocentric Vision
Alan Yu, Yun Chang, Christopher Xie +1
Robotic mapping systems typically approach building metric-semantic scene representations from the robot's own sensors and cameras. However, these "first person" maps inherit the r…
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…