3D-C2FT: Coarse-to-fine Transformer for Multi-view 3D Reconstruction

• URL: http://arxiv.org/abs/2205.14575v1
• Date: Sun, 29 May 2022 06:01:42 GMT
• Title: 3D-C2FT: Coarse-to-fine Transformer for Multi-view 3D Reconstruction
• Authors: Leslie Ching Ow Tiong, Dick Sigmund, Andrew Beng Jin Teoh
• Abstract summary: This paper proposes a new model, namely 3D coarse-to-fine transformer (3D-C2FT), 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. Experimental results show that 3D-C2FT achieves notable results and outperforms several competing models on these datasets.
• Score: 14.89364490991374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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