In recent years, learning-based approaches for 3D reconstruction have gained much popularity due to their encouraging results. However, unlike 2D images, 3D cannot be represented in its canonical form to make it computationally lean and memory-efficient. Moreover, the generation of a 3D model directly from a single 2D image is even more challenging due to the limited details available from the image for 3D reconstruction. Existing learning-based techniques still lack the desired resolution, efficiency, and smoothness of the 3D models required for many practical applications. In this paper, we propose voxel-based 3D object reconstruction (V3DOR) from a single 2D image for better accuracy, one using autoencoders (AE) and another using variational autoencoders (VAE). The encoder part of both models is used to learn suitable compressed latent representation from a single 2D image, and a decoder generates a corresponding 3D model. Our contribution is twofold. First, to the best of the authors’ knowledge, it is the first time that variational autoencoders (VAE) have been employed for the 3D reconstruction problem. Second, the proposed models extract a discriminative set of features and generate a smoother and high-resolution 3D model. To evaluate the efficacy of the proposed method, experiments have been conducted on a benchmark ShapeNet data set. The results confirm that the proposed method outperforms state-of-the-art methods.

中文翻译：

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使用变分自动编码器从单个 2D 图像重建基于体素的 3D 对象

近年来，基于学习的 3D 重建方法因其令人鼓舞的结果而广受欢迎。然而，与 2D 图像不同，3D 不能以其规范形式表示，以使其计算量小且内存高效。此外，由于可用于 3D 重建的图像细节有限，直接从单个 2D 图像生成 3D 模型更具挑战性。现有的基于学习的技术仍然缺乏许多实际应用所需的 3D 模型所需的分辨率、效率和平滑度。在本文中，我们从单个 2D 图像中提出了基于体素的 3D 对象重建 (V3DOR)，以提高准确性，一个使用自动编码器 (AE)，另一个使用变分自动编码器 (VAE)。两个模型的编码器部分用于从单个 2D 图像中学习合适的压缩潜在表示，解码器生成相应的 3D 模型。我们的贡献是双重的。首先，据作者所知，这是第一次将变分自编码器 (VAE) 用于 3D 重建问题。其次，所提出的模型提取一组判别特征并生成更平滑和高分辨率的 3D 模型。为了评估所提出方法的有效性，已经在基准 ShapeNet 数据集上进行了实验。结果证实，所提出的方法优于最先进的方法。这是首次将变分自编码器 (VAE) 用于 3D 重建问题。其次，所提出的模型提取一组判别特征并生成更平滑和高分辨率的 3D 模型。为了评估所提出方法的有效性，已经在基准 ShapeNet 数据集上进行了实验。结果证实，所提出的方法优于最先进的方法。这是首次将变分自编码器 (VAE) 用于 3D 重建问题。其次，所提出的模型提取一组判别特征并生成更平滑和高分辨率的 3D 模型。为了评估所提出方法的有效性，已经在基准 ShapeNet 数据集上进行了实验。结果证实，所提出的方法优于最先进的方法。