Monocular 3D Semantic Scene Completion (SSC) has garnered significant
attention in recent years due to its potential to predict complex semantics and
geometry shapes from a single image, requiring no 3D inputs. In this paper, we
identify several critical issues in current state-of-the-art methods, including
the Feature Ambiguity of projected 2D features in the ray to the 3D space, the
Pose Ambiguity of the 3D convolution, and the Computation Imbalance in the 3D
convolution across different depth levels. To address these problems, we devise
a novel Normalized Device Coordinates scene completion network (NDC-Scene) that
directly extends the 2D feature map to a Normalized Device Coordinates (NDC)
space, rather than to the world space directly, through progressive restoration
of the dimension of depth with deconvolution operations. Experiment results
demonstrate that transferring the majority of computation from the target 3D
space to the proposed normalized device coordinates space benefits monocular
SSC tasks. Additionally, we design a Depth-Adaptive Dual Decoder to
simultaneously upsample and fuse the 2D and 3D feature maps, further improving
overall performance. Our extensive experiments confirm that the proposed method
consistently outperforms state-of-the-art methods on both outdoor SemanticKITTI
and indoor NYUv2 datasets. Our code are available at
this https URL

通过解决二维特征的模糊性、三维卷积的姿态模糊性和三维卷积在不同深度层次上的计算不平衡等问题，我们提出了一种新的标准化设备坐标场景补全网络（NDC-Scene），通过逐步恢复深度的卷积操作，将二维特征映射直接扩展到标准化设备坐标（NDC）空间，而不是直接映射到世界空间。实验证明，在目标三维空间与 NDC 空间之间进行大部分计算转换有助于单目 SSC 任务的性能提升。此外，我们设计了一种深度自适应的双解码器，同时上采样和融合二维和三维特征图，进一步提高了整体性能。在室外 SemanticKITTI 和室内 NYUv2 数据集上的广泛实验证实，我们提出的方法始终优于最先进的方法。