  `3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal
undertaking in autonomous driving, aiming to predict voxel occupancy within
volumetric scenes. However, prevailing methodologies primarily focus on
voxel-wise feature aggregation, while neglecting instance semantics and scene
context. In this paper, we present a novel paradigm termed Symphonies
(Scene-from-Insts), that delves into the integration of instance queries to
orchestrate 2D-to-3D reconstruction and 3D scene modeling. Leveraging our
proposed Serial Instance-Propagated Attentions, Symphonies dynamically encodes
instance-centric semantics, facilitating intricate interactions between
image-based and volumetric domains. Simultaneously, Symphonies enables holistic
scene comprehension by capturing context through the efficient fusion of
instance queries, alleviating geometric ambiguity such as occlusion and
perspective errors through contextual scene reasoning. Experimental results
demonstrate that Symphonies achieves state-of-the-art performance on
challenging benchmarks SemanticKITTI and SSCBench-KITTI-360, yielding
remarkable mIoU scores of 15.04 and 18.58, respectively. These results showcase
the paradigm's promising advancements. The code is available at
https://github.com/hustvl/Symphonies.
