Semantic Scene Completion (SSC) aims to simultaneously predict the volumetric
occupancy and semantic category of a 3D scene. It helps intelligent devices to
understand and interact with the surrounding scenes. Due to the high-memory
requirement, current methods only produce low-resolution completion
predictions, and generally lose the object details. Furthermore, they also
ignore the multi-scale spatial contexts, which play a vital role for the 3D
inference. To address these issues, in this work we propose a novel deep
learning framework, named Cascaded Context Pyramid Network (CCPNet), to jointly
infer the occupancy and semantic labels of a volumetric 3D scene from a single
depth image. The proposed CCPNet improves the labeling coherence with a
cascaded context pyramid. Meanwhile, based on the low-level features, it
progressively restores the fine-structures of objects with Guided Residual
Refinement (GRR) modules. Our proposed framework has three outstanding
advantages: (1) it explicitly models the 3D spatial context for performance
improvement; (2) full-resolution 3D volumes are produced with
structure-preserving details; (3) light-weight models with low-memory
requirements are captured with a good extensibility. Extensive experiments
demonstrate that in spite of taking a single-view depth map, our proposed
framework can generate high-quality SSC results, and outperforms
state-of-the-art approaches on both the synthetic SUNCG and real NYU datasets.

本研究提出了一种新的深度学习框架 CCNPNet，它采用级联金字塔结构模型和引导型残差细化模块，可从单张深度图像中推断出体素化 3D 场景的占用率和语义标签，并在 SUNCG 和 NYU 数据集上取得了优于现有方法的高质量 SSC 结果。

级联上下文金字塔用于全分辨率 3D 语义场景补全

Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene  Completion

We introduce a View-Volume convolutional neural network (VVNet) for inferring
the occupancy and semantic labels of a volumetric 3D scene from a single depth
image. The VVNet concatenates a 2D view CNN and a 3D volume CNN with a
differentiable projection layer. Given a single RGBD image, our method extracts
the detailed geometric features from the input depth image with a 2D view CNN
and then projects the features into a 3D volume according to the input depth
map via a projection layer. After that, we learn the 3D context information of
the scene with a 3D volume CNN for computing the result volumetric occupancy
and semantic labels. With combined 2D and 3D representations, the VVNet
efficiently reduces the computational cost, enables feature extraction from
multi-channel high resolution inputs, and thus significantly improves the
result accuracy. We validate our method and demonstrate its efficiency and
effectiveness on both synthetic SUNCG and real NYU dataset.

本文介绍了一种名为 VVNet 的卷积神经网络，可从单个深度图像中推断出体积三维场景的占用率和语义标签，VVNet 有效地降低了计算成本，提高了结果的准确性。