arXiv Computer Vision
SplatLabel: Pseudo-Labelling through 4D Gaussian Splatting
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RoSe-SLAM: Robust Semantic-Aware Gaussian Splatting SLAM from Dynamic Monocular Videos
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Bootstrapping a 4D LiDAR Annotation Tool from Video Foundation Models
The paper introduces LiDAR‑SAM2, a framework that converts the 2D video foundation model SAM2 into a scalable source of supervision for 4D LiDAR data. By projecting SAM2 video masks into multi‑view LiDAR space and aggregating them temporally, the method automatically generates temporally coherent LiDAR labels without human annotation. Experiments on SemanticKITTI show that these automatically produced semantic and panoptic labels achieve quality close to full human annotation, enabling models trained on them to approach the performance of fully supervised systems.
arXiv Computer Vision
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Geometry Without Coordinates: LiDAR Diffusion as a 3D Feature Bridge
arXiv:2609.10322v1 Announce Type: new Abstract: Transferring the rich priors of large 2D foundation models to sparse 3D LiDAR remains challenging, as training native 3D foundation models at comparabl...
arXiv Computer Vision
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Stream3Dv2: Geometric-Semantic Fusion Enhanced Streaming Zero-Shot 3D Scene Understanding
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GaussianDS: Depth-supervised Semantic Gaussian Splatting for Scene Understanding
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arXiv Computer Vision
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Spheriverse: 3D Scene Understanding from Spherical Observations in the Wild
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