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Found 5 papers, 5 papers with code
MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaption in 3D Object Detection
MS3D++ provides a straightforward approach to domain adaptation by generating high-quality pseudo-labels, enabling the adaptation of 3D detectors to a diverse range of lidar types, regardless of their density.
MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection
Our proposed Kernel-Density Estimation (KDE) Box Fusion method fuses box proposals from multiple domains to obtain pseudo-labels that surpass the performance of the best source domain detectors.
Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
With SEE-VCN, we obtain a unified representation of objects across datasets, allowing the network to focus on learning geometry, rather than overfitting on scan patterns.
See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation
Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects.
Optimising the selection of samples for robust lidar camera calibration
We propose a robust calibration pipeline that optimises the selection of calibration samples for the estimation of calibration parameters that fit the entire scene.
