3D-VRVT: 3D Voxel Reconstruction from A Single Image with Vision Transformer

Xi Li, Ping Kuang
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引用次数: 2

Abstract

Deep CNN methods have shown very competitive performance in 3D voxel reconstruction from single-view synthetic clean-background images. However, how to generate the target object from a real-world image with clutter background is rarely studied. In this paper, we present a novel network named 3D-VRVT for 3D voxel reconstruction from a single image. Unlike pure CNN-based methods in the past, our 3D-VRVT extracts region features with Vision Transformer (ViT) encoder based on self-attention mechanism, and then a well-designed voxel decoder is used to generate three-dimensional voxel from the encoded image features. The experimental results show that our 3D-VRVT can reconstruct 3D voxel from both synthetic clean-background and real-world images effectively.
3D- vrvt:基于视觉转换器的单幅图像三维体素重建
深度CNN方法在单视图合成干净背景图像的三维体素重建中显示出非常有竞争力的性能。然而,如何从具有杂波背景的真实图像中生成目标物体却鲜有研究。本文提出了一种新的3D- vrvt网络,用于单幅图像的三维体素重建。与以往纯基于cnn的方法不同,我们的3D-VRVT采用基于自注意机制的视觉变换(Vision Transformer, ViT)编码器提取区域特征,然后使用设计良好的体素解码器从编码后的图像特征中生成三维体素。实验结果表明,我们的3D- vrvt可以有效地从合成的干净背景图像和真实图像中重建三维体素。
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