SLCF-Net is a pioneering method that combines RGB images and sparse LiDAR scans to infer a 3D voxelized semantic scene. The model leverages Gaussian-decay Depth-prior Projection for feature projection and inter-frame feature propagation for temporal consistency. By integrating historical information, SLCF-Net excels in both accuracy and consistency metrics on the SemanticKITTI dataset. The model's performance surpasses other SSC baselines, showcasing its effectiveness in semantic scene completion tasks.
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