Semantic Scene Completion (SSC) aims to jointly predict se-
mantic categories and 3D occupancy of a scene from coarse
inputs, which is crucial for providing reliable perception in
autonomous driving. In this paper, we enhance existing SSC
models by unveiling the vanishing point region, specifically
addressing challenges posed by tiny objects and voxels dis-
tant from the monocular camera. At the core of our method,
we propose the Vanishing Point Aggregator (VPA) to prior-
itize features in high-density central areas. The proposed
VPA seamlessly integrates the Vanishing Point Query (VPQ)
with the vanilla instance query via a cross-attention fusion
mechanism to refine feature representation. To evaluate the
effectiveness of our method, we conduct comprehensive ex-
periments on two standard SSC benchmarks and demonstrate
that our method achieves SOTA performance. Our approach
significantly improves the performance across various se-
mantic classes, including a notable gain of 0.37 mIoU on
SemanticKITTI and 0.5 mIoU on SSCBench-KITTI-360 for
tiny objects. Ablation studies further validate the efficacy of
our innovative query fusion strategy, showcasing its capabil-
ity in long-range predictions for SSC tasks.