Recently, the transformer model has been successfully employed for the
multi-view 3D reconstruction problem. However, challenges remain on designing
an attention mechanism to explore the multiview features and exploit their
relations for reinforcing the encoding-decoding modules. This paper proposes a
new model, namely 3D coarse-to-fine transformer (3D-C2FT), by introducing a
novel coarse-to-fine(C2F) attention mechanism for encoding multi-view features
and rectifying defective 3D objects. C2F attention mechanism enables the model
to learn multi-view information flow and synthesize 3D surface correction in a
coarse to fine-grained manner. The proposed model is evaluated by ShapeNet and
Multi-view Real-life datasets. Experimental results show that 3D-C2FT achieves
notable results and outperforms several competing models on these datasets.

提出了一种新的模型，即 3D - 粗到细注意力变压器 (3D-C2FT)，通过引入一种新颖的粗到细 (C2F) 注意力机制来编码多视图特征和矫正有缺陷的 3D 对象，以粗到细的方式学习多视图信息流，并在 ShapeNet 和 Multi-view 实际数据集上取得了显著的结果，优于竞争模型。