论文标题
3Dilg：3D生成建模的不规则潜在网格
3DILG: Irregular Latent Grids for 3D Generative Modeling
论文作者
论文摘要
我们提出了一种新的表示，将3D形状编码为神经场。该表示形式旨在与变压器架构兼容，并使形状重建和形状产生有益。关于神经领域的现有作品是基于网格的表示，在常规网格上定义了潜伏期。相比之下，我们定义了潜在的网格，使我们的表示能够稀疏和适应性。在点云的形状重建背景下，我们基于不规则网格建立的形状表示从基于网格的方法上改善了重建精度。对于形状产生，我们的表示使用自动折射概率模型促进了高质量的形状生成。我们展示了不同的应用程序，这些应用比当前的现行状态有所改善。首先，我们从单个高分辨率图像中显示了概率形状重建的结果。其次，我们训练一个以非常低的分辨率图像为条件的概率模型。第三，我们将模型应用于类别条件的生成。所有概率实验都证实，我们能够生成详细和高质量的形状，以在生成3D形状建模中产生新的最新状态。
We propose a new representation for encoding 3D shapes as neural fields. The representation is designed to be compatible with the transformer architecture and to benefit both shape reconstruction and shape generation. Existing works on neural fields are grid-based representations with latents defined on a regular grid. In contrast, we define latents on irregular grids, enabling our representation to be sparse and adaptive. In the context of shape reconstruction from point clouds, our shape representation built on irregular grids improves upon grid-based methods in terms of reconstruction accuracy. For shape generation, our representation promotes high-quality shape generation using auto-regressive probabilistic models. We show different applications that improve over the current state of the art. First, we show results for probabilistic shape reconstruction from a single higher resolution image. Second, we train a probabilistic model conditioned on very low resolution images. Third, we apply our model to category-conditioned generation. All probabilistic experiments confirm that we are able to generate detailed and high quality shapes to yield the new state of the art in generative 3D shape modeling.
