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George Vosselman

7 accepted papers

2026

ACPV-Net: All-Class Polygonal Vectorization for Seamless Vector Map Generation from Aerial Imagery

CVPR 2026

We tackle the problem of generating a complete vector map representation from aerial imagery in a single run: producing polygons for all land-cover classes with shared boundaries and without gaps or overlaps. Existing polygonization methods are typically class-specific; extending them to multiple cl

Cited by 0SourcecodeScholar
2026

Bridge: Basis-Driven Causal Inference Marries VFMs for Domain Generalization

CVPR 2026

Detectors often suffer from degraded performance, primarily due to the distributional gap between the source and target domains. This issue is especially evident in single-source domains with limited data, as models tend to rely on confounders (e.g., illumination, co-occurrence, and style) from the

Cited by 0SourcecodeScholar
2026

Gaussian or Plane? Both: Semantic-Driven Voxel Representation for LiDAR-Inertial Odometry

RA-L 2026

Accurate LiDAR-inertial odometry (LIO) highly depends on the geometric fidelity of the underlying environment representation. We explore the new and interesting research direction of integrating semantic segmentation models into metric odometry algorithms to enrich their representational capacity. S

Cited by 2SourceScholar
2026

Gaussian or Plane? Both: Semantic-Driven Voxel Representation for LiDAR–Inertial Odometry

ICRA 2026poster

Accurate LiDAR-inertial odometry (LIO) highly depends on the geometric fidelity of the underlying environment representation. We explore the new and interesting research direction of integrating semantic segmentation models into metric odometry algorithms to enrich their representational capacity. S…

Cited by 0SourceScholar
2025

DVLO4D: Deep Visual-Lidar Odometry with Sparse Spatial-Temporal Fusion

ICRA 2025

Visual-LiDAR odometry is a critical component for autonomous system localization, yet achieving high accuracy and strong robustness remains a challenge. Traditional approaches commonly struggle with sensor misalignment, fail to fully leverage temporal information, and require extensive manual tuning

Cited by 2SourceScholar
2025

M2H: Multi-Task Learning with Efficient Window-Based Cross-Task Attention for Monocular Spatial Perception

IROS 2025

Deploying real-time spatial perception on edge devices requires efficient multi-task models that leverage complementary task information while minimizing computational overhead. In this paper, we introduce Multi-Mono-Hydra (M2H), a novel multi-task learning framework designed for semantic segmentati

Cited by 1SourcecodeScholar
2023

Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation

CVPR 2023poster

Self-supervised monocular depth estimation that does not require ground truth for training has attracted attention in recent years. It is of high interest to design lightweight but effective models so that they can be deployed on edge devices. Many existing architectures benefit from using heavier b…