Symphonize 3D Semantic Scene Completion with Contextual Instance Queries

27 Jun 2023  ·  Haoyi Jiang, Tianheng Cheng, Naiyu Gao, Haoyang Zhang, Tianwei Lin, Wenyu Liu, Xinggang Wang ·

`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

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Semantic Scene Completion KITTI-360 Symphonies mIoU 18.58 # 1
3D Semantic Scene Completion from a single RGB image KITTI-360 Symphonies mIoU 18.58 # 2
3D Semantic Scene Completion SemanticKITTI Symphonies (RGB input only) mIoU 15.04 # 14
3D Semantic Scene Completion from a single RGB image SemanticKITTI Symphonies mIoU 15.04 # 2