FSTM improves indoor reconstruction by training geometry first without semantic supervision, then adding semantics, achieving 2.3x faster training and higher object surface recall than joint optimization.
Panoptic nerf: 3d-to-2d label transfer for panoptic urban scene segmentation
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First Shape, Then Meaning: Efficient Geometry and Semantics Learning for Indoor Reconstruction
FSTM improves indoor reconstruction by training geometry first without semantic supervision, then adding semantics, achieving 2.3x faster training and higher object surface recall than joint optimization.