  In the realm of autonomous vehicle (AV) perception, comprehending 3D scenes
is paramount for tasks such as planning and mapping. Semantic scene completion
(SSC) aims to infer scene geometry and semantics from limited observations.
While camera-based SSC has gained popularity due to affordability and rich
visual cues, existing methods often neglect the inherent uncertainty in models.
To address this, we propose an uncertainty-aware camera-based 3D semantic scene
completion method ($\alpha$-SSC). Our approach includes an uncertainty
propagation framework from depth models (Depth-UP) to enhance geometry
completion (up to 11.58% improvement) and semantic segmentation (up to 14.61%
improvement). Additionally, we propose a hierarchical conformal prediction
(HCP) method to quantify SSC uncertainty, effectively addressing high-level
class imbalance in SSC datasets. On the geometry level, we present a novel KL
divergence-based score function that significantly improves the occupied recall
of safety-critical classes (45% improvement) with minimal performance overhead
(3.4% reduction). For uncertainty quantification, we demonstrate the ability to
achieve smaller prediction set sizes while maintaining a defined coverage
guarantee. Compared with baselines, it achieves up to 85% reduction in set
sizes. Our contributions collectively signify significant advancements in SSC
accuracy and robustness, marking a noteworthy step forward in autonomous
perception systems.
