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SplatLabel: Pseudo-Labelling through 4D Gaussian Splatting
While 2D Vision Foundation Models offer a pathway to automate 3D semantic pseudo-labelling, translating these priors into robust 3D representations typically requires complex heuristics or multi-model ensembles. We introduce SplatLabel, an automated pipeline that leverages a 4D Gaussian representation to extract LiDAR segmentation with predictive confidence, as well as semantic occupancy grids at arbitrary voxel resolutions. At its core, SplatLabel handles dynamic environments through an explicit temporal manifold that models the trajectories and lifespans of individual 3D primitives. This all
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- arXiv · AI, language, vision and robotics · 2026-09-24T14:05:59.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.