提出自适应序列化变压器，提升室内单目语义场景补全精度
AdaSFormer: Adaptive Serialized Transformers for Monocular Semantic Scene Completion from Indoor Environments
- 通过可学习偏移动态调整感受野，实现高效特征建模
- 在NYUv2和Occ-ScanNet上达到当前最佳性能
- 适合关注室内场景重建与视觉理解的研究者
室内单目语义场景补全（MSSC）因复杂空间布局和严重遮挡，比室外更具挑战性。尽管变换器擅长建模全局依赖，但其高内存开销及难以恢复细粒度细节的问题限制了其在室内MSSC中的应用。为此，我们提出AdaSFormer，一种专为室内MSSC设计的序列化变换器框架。模型包含三项关键设计：(1) 自适应序列化变换器，通过可学习偏移动态调节感受野；(2) 中心相对位置编码，捕捉丰富的空间信息；(3) 卷积调制层归一化，弥合卷积与变换器特征间的异构表示差异。在NYUv2和Occ-ScanNet上的大量实验表明，AdaSFormer达到当前最优性能。代码已公开于：https://github.com/alanWXZ/AdaSFormer。
原文摘要 · Abstract (English)
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 AdaSFormer, 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 AdaSFormer achieves state-of-the-art performance. The code is publicly available at: https://github.com/alanWXZ/AdaSFormer.
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