论文标题
部分可观测时空混沌系统的无模型预测
3D-C2FT: Coarse-to-fine Transformer for Multi-view 3D Reconstruction
论文作者
论文摘要
最近，变压器模型已成功用于多视图3D重建问题。然而，在设计注意机制以探索多视图特征并利用其关系来加强编码模块的模块方面仍然存在挑战。本文提出了一种新的模型，即3D粗到1个变压器（3D-C2FT），通过引入一种新颖的粗到五（C2F）注意机制，用于编码多视图特征和纠正有缺陷的3D对象。 C2F注意机制使模型能够以粗到细粒的方式学习多视图信息流并合成3D表面校正。提出的模型由Shapenet和多视图现实生活数据集评估。实验结果表明，3D-C2FT取得了显着的结果，并且在这些数据集上表现优于几个竞争模型。
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.
