让摄像头看不远的区域，提升3D场景补全精度。
Towards Temporal Fusion Beyond the Field of View for Camera-based Semantic Scene Completion
- 用当前帧和历史帧点云对齐生成隐藏区域特征
- 在SemanticKITTI上比顶尖方法提升4.2%（mIoU）
- 适合自动驾驶中长尾场景重建任务
基于摄像头的3D语义场景补全（SSC）方法近年越来越依赖时序信息来丰富当前帧特征。然而，这些方法主要关注帧内区域增强，难以重建车辆侧方视野外的关键区域，尽管前一帧常包含这些未见区域的有用上下文。为此，我们提出以当前帧为中心的上下文3D融合模块（C3DFusion），通过显式对齐当前帧与历史帧的3D提升点特征，生成感知隐藏区域的3D特征几何结构。C3DFusion采用两种互补技术——历史上下文模糊与当前中心特征稠密化——前者通过降低历史点特征尺度抑制误匹配噪声，后者通过增加当前点特征的体素贡献实现增强。该模块可无缝集成至标准SSC架构，在SemanticKITTI和SSCBench-KITTI-360数据集上显著优于现有方法，且具备强泛化能力，在其他基线模型上也取得明显性能提升。
原文摘要 · Abstract (English)
Recent camera-based 3D semantic scene completion (SSC) methods have increasingly explored leveraging temporal cues to enrich the features of the current frame. However, while these approaches primarily focus on enhancing in-frame regions, they often struggle to reconstruct critical out-of-frame areas near the sides of the ego-vehicle, although previous frames commonly contain valuable contextual information about these unseen regions. To address this limitation, we propose the Current-Centric Contextual 3D Fusion (C3DFusion) module, which generates hidden region-aware 3D feature geometry by explicitly aligning 3D-lifted point features from both current and historical frames. C3DFusion performs enhanced temporal fusion through two complementary techniques-historical context blurring and current-centric feature densification-which suppress noise from inaccurately warped historical point features by attenuating their scale, and enhance current point features by increasing their volumetric contribution. Simply integrated into standard SSC architectures, C3DFusion demonstrates strong effectiveness, significantly outperforming state-of-the-art methods on the SemanticKITTI and SSCBench-KITTI-360 datasets. Furthermore, it exhibits robust generalization, achieving notable performance gains when applied to other baseline models.
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