3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal task
for autonomous driving, as it involves predicting per-voxel occupancy within a
3D scene from partial LiDAR or image inputs. Existing methods primarily focus
on the voxel-wise feature aggregation, while neglecting the instance-centric
semantics and broader context. In this paper, we present a novel paradigm
termed Symphonies (Scene-from-Insts) for SSC, which completes the scene volume
from a sparse set of instance queries derived from the input with context
awareness. By incorporating the queries as the instance feature representations
within the scene, Symphonies dynamically encodes the instance-centric semantics
to interact with the image and volume features while avoiding the dense
voxel-wise modeling. Simultaneously, it orchestrates a more comprehensive
understanding of the scenario by capturing context throughout the entire scene,
contributing to alleviating the geometric ambiguity derived from occlusion and
perspective errors. Symphonies achieves a state-of-the-art result of 13.02 mIoU
on the challenging SemanticKITTI dataset, outperforming existing methods and
showcasing the promising advancements of the paradigm. The code is available at
https://github.com/hustvl/Symphonies.

提出了一种名为 Symphonies 的新范式来实现 3D 语义场景完形填空任务，它通过从具有上下文感知的输入中派生的一组稀疏的实例查询来完成场景卷积物的构建，并通过在场景内部使用查询作为实例特征表示来交互影响图像和卷积物特征，提高对整个情境的综合理解，能够减轻由遮挡和透视误差产生的几何模糊，同时在具有挑战性的 SemanticKITTI 数据集上实现了 13.02 mIoU 的最佳效果。