  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
https://github.com/Jiawei-Yao0812/NDCScene.
