2 citations · 3 across the 8 of their papers we have counts for
11 papers
MixPT: Point Transformer with Interleaved Window and Global Attention
Minkyun Seo, Hyungtae Lim, Jaesik Park
Point cloud transformers aim to capture detailed local geometry and relationships across a scene at a manageable computational cost. Two common approaches are window attention and…
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…
Commerge: Communication-Efficient, Robust, and Fast LiDAR Map Merging Framework for Multi-Robot Coordination in Resource-Constrained Scenarios
Hogyun Kim, Jiwon Choi, Juwon Kim +4
By maintaining global consistency across robot teams, multi-robot LiDAR map merging enables faster exploration and efficient area coverage. However, map merging requires exchanging…
Technical Report for ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Leveraging DINOv3 for Robust Outdoor Scene Understanding in Field Robotics
Jaeil Park, Hyobin Choi, Sangjin Lee +2
The GOOSE 2D Fine-Grained Semantic Segmentation Challenge at the ICRA 2026 Workshop on Field Robotics evaluates dense semantic segmentation of off-road imagery over a fine-grained…
GenZ-LIO: Generalizable LiDAR-Inertial Odometry Beyond Confined--Open Boundaries
Daehan Lee, Hyungtae Lim, Seongjun Kim +7
For field robotic missions such as inspection, search-and-rescue, and exploration, light detection and ranging (LiDAR)-inertial odometry (LIO) can serve as a core component of auto…
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…
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…
SKiD-SLAM: Robust, Lightweight, and Distributed Multi-Robot LiDAR SLAM in Resource-Constrained Field Environments
Hogyun Kim, Jiwon Choi, Juwon Kim +4
Distributed LiDAR SLAM is crucial for achieving efficient robot autonomy and improving the scalability of mapping. However, two issues need to be considered when applying it in fie…
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…
Outlier-Robust Long-Term Robotic Mapping Leveraging Ground Segmentation
Hyungtae Lim
Despite the remarkable advancements in deep learning-based perception technologies and simultaneous localization and mapping (SLAM), one can face the failure of these approaches wh…