To solve the decoder bottleneck in sparse 3D occupancy prediction, we propose SPOT-Occ. It uses a prototype-based sparse Transformer decoder, replacing costly dense attention with a "guided selection + focused aggregation" process. Queries focus on a small set of key "prototypes". The final model is significantly faster and more accurate.
☆18Feb 5, 2026Updated 6 months ago
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