Semantic Scene Completion (SSC) refers to the task of inferring the 3D
semantic segmentation of a scene while simultaneously completing the 3D shapes.
We propose PALNet, a novel hybrid network for SSC based on single depth. PALNet
utilizes a two-stream network to extract both 2D and 3D features from
multi-stages using fine-grained depth information to efficiently captures the
context, as well as the geometric cues of the scene. Current methods for SSC
treat all parts of the scene equally causing unnecessary attention to the
interior of objects. To address this problem, we propose Position Aware
Loss(PA-Loss) which is position importance aware while training the network.
Specifically, PA-Loss considers Local Geometric Anisotropy to determine the
importance of different positions within the scene. It is beneficial for
recovering key details like the boundaries of objects and the corners of the
scene. Comprehensive experiments on two benchmark datasets demonstrate the
effectiveness of the proposed method and its superior performance. Models and
Video demo can be found at: this https URL

本文提出了基于单个深度输入的混合网络 PALNet，利用两个流提取 2D 和 3D 特征，使用细粒度深度信息有效捕获场景的上下文和几何线索，并提出了 Position Aware Loss 以确定不同位置的重要性，从而使得场景的不同部分能够得到更好的恢复。文章的实验结果表明了所提出方法的有效性及卓越性能。