`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.

翻译：3D语义场景补全（SSC）已成为自动驾驶领域一项新兴且关键的任务，旨在预测体素场景中的体素占用情况。然而，现有方法主要关注基于体素的特征聚合，而忽视了实例语义和场景上下文。本文提出了一种名为Symphonies（Scene-from-Insts）的新范式，通过引入实例查询来协调2D到3D重建与3D场景建模。利用我们提出的串行实例传播注意力机制，Symphonies动态编码以实例为中心的语义，促进基于图像和基于体素域之间的复杂交互。同时，Symphonies通过实例查询的高效融合捕捉上下文，实现整体场景理解，并通过上下文场景推理缓解遮挡和视角误差等几何歧义。实验结果表明，Symphonies在具有挑战性的基准测试SemanticKITTI和SSCBench-KITTI-360上取得了最先进的性能，分别获得了15.04和18.58的显著mIoU分数。这些结果展示了该范式的显著进展。代码已开源：https://github.com/hustvl/Symphonies。