Exploring Easy Boosts for Lidar Semantic Scene Completion
This paper investigates "free lunch" strategies to boost the performance of lidar semantic scene completion (SSC) without requiring complex architectural redesigns. We first demonstrate that endowing input point clouds with semantic pseudo-labels from off-the-shelf segmentors significantly improves the performance of existing architectures. By evaluating these models against an oracle, we establish that high-quality semantic priors are a primary driver of mIoU gains. Furthermore, we equip the input lidar scan with visibility information that distinguishes between empty and unknown spaces, which provides a secondary performance boost across the tested architectures. Using these simple enha...
| Authors | Tetiana Martyniuk, Jonathan Seele, Alexandre Boulch et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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