  3D semantic scene completion (SSC) is an ill-posed perception task that
requires inferring a dense 3D scene from limited observations. Previous
camera-based methods struggle to predict accurate semantic scenes due to
inherent geometric ambiguity and incomplete observations. In this paper, we
resort to stereo matching technique and bird's-eye-view (BEV) representation
learning to address such issues in SSC. Complementary to each other, stereo
matching mitigates geometric ambiguity with epipolar constraint while BEV
representation enhances the hallucination ability for invisible regions with
global semantic context. However, due to the inherent representation gap
between stereo geometry and BEV features, it is non-trivial to bridge them for
dense prediction task of SSC. Therefore, we further develop a unified
occupancy-based framework dubbed BRGScene, which effectively bridges these two
representations with dense 3D volumes for reliable semantic scene completion.
Specifically, we design a novel Mutual Interactive Ensemble (MIE) block for
pixel-level reliable aggregation of stereo geometry and BEV features. Within
the MIE block, a Bi-directional Reliable Interaction (BRI) module, enhanced
with confidence re-weighting, is employed to encourage fine-grained interaction
through mutual guidance. Besides, a Dual Volume Ensemble (DVE) module is
introduced to facilitate complementary aggregation through channel-wise
recalibration and multi-group voting. Our method outperforms all published
camera-based methods on SemanticKITTI for semantic scene completion. Our code
is available on https://github.com/Arlo0o/StereoScene.
