AdaSFormer：面向室内环境单目语义场景补全的自适应序列化 Transformer
AdaSFormer: Adaptive Serialized Transformers for Monocular Semantic Scene Completion from Indoor Environments
面向室内单目语义场景补全，用可学习偏移的自适应序列化 Transformer 动态调整感受野，并用中心相对位置编码与卷积调制 LayerNorm 融合卷积与 Transformer 特征，可直接迁移到其他需要从单张图恢复稠密三维语义的室内重建任务。
原文依据 an Adaptive Serialized Transformer with learnable shifts that dynamically adjust receptive fields
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论室内单目语义场景补全（MSSC）由于空间布局复杂且遮挡严重，明显比室外场景更具挑战性。尽管 Transformer 适合建模全局依赖关系，但其高昂的内存开销以及难以重建细粒度细节的问题，限制了其在室内 MSSC 中的应用。为解决这些局限，我们提出 ASFormer，一个专为室内 MSSC 定制的序列化 Transformer 框架。该模型包含三项关键设计：（1）带有可学习偏移、能够动态调整感受野的自适应序列化 Transformer；（2）捕捉空间信息丰富度的中心相对位置编码；（3）衔接卷积特征与 Transformer 特征之间异构表示的卷积调制层归一化。在 NYUv2 和 Occ-ScanNet 上的大量实验表明，ASFormer 取得了最优性能。代码将公开。
英文原文摘要 979 字符
Indoor monocular semantic scene completion (MSSC) is notably more challenging than its outdoor counterpart due to complex spatial layouts and severe occlusions. While transformers are well suited for modeling global dependencies, their high memory cost and difficulty in reconstructing fine-grained details have limited their use in indoor MSSC. To address these limitations, we introduce ASFormer, a serialized transformer framework tailored for indoor MSSC. Our model features three key designs: (1) an Adaptive Serialized Transformer with learnable shifts that dynamically adjust receptive fields; (2) a Center-Relative Positional Encoding that captures spatial information richness; and (3) a Convolution-Modulated Layer Normalization that bridges heterogeneous representations between convolutional and transformer features. Extensive experiments on NYUv2 and Occ-ScanNet demonstrate that ASFormer achieves state-of-the-art performance. Code will be made publicly available.