The three-dimensional High Efficiency Video Coding (3D-HEVC) standard is an extension of the High Efficiency Video Coding (HEVC) standard which is the latest three-dimensional (3D) video coding standard available. Based on the HEVC standard, 3D-HEVC adds some advanced techniques that are conducive to depth map coding at the expense of a significant increase in coding complexity. In this paper, a Swin-CNN network is proposed, which leverages the advantages of Swin Transformer in extracting global information and convolutional neural network (CNN) in extracting local information. Through Swin-CNN, the coding tree unit (CTU) partition structure in depth intra coding (DIC) can be predicted accurately. In addition, we construct a large-scale depth map dataset to train the Swin-CNN. Finally, we use the proposed algorithm to replace the search process of CTU quadtree partition in DIC. Experimental results show that the proposed algorithm can reduce the coding time by 60.9% to 67.5% without compromising the quality of the synthesised views, effectively reducing the coding complexity of 3D-HEVC.

三维高效视频编码 (3D-HEVC) 标准是高效视频编码 (HEVC) 标准的扩展，后者是最新的三维 (3D) 视频编码标准。 3D-HEVC在HEVC标准的基础上，增加了一些有利于深度图编码的先进技术，但代价是编码复杂度大幅增加。本文提出了一种Swin-CNN网络，它利用了Swin Transformer在提取全局信息方面和卷积神经网络（CNN）在提取局部信息方面的优势。通过Swin-CNN，可以准确预测深度帧内编码（DIC）中的编码树单元（CTU）分区结构。此外，我们构建了一个大规模深度图数据集来训练 Swin-CNN。最后，我们使用所提出的算法来代替DIC中CTU四叉树划分的搜索过程。实验结果表明，该算法在不影响合成视图质量的情况下，可以将编码时间减少60.9%~67.5%，有效降低3D-HEVC的编码复杂度。