World models have garnered increasing attention for comprehensive modeling of the real world. However, most existing methods still rely on pixel-aligned representations as the basis for world evolution, neglecting the inherent 3D nature of the physical world. This could undermine the 3D consistency and diminish the modeling efficiency of world models. In this paper, we present Terra, a native 3D world model that represents and generates explorable environments in an intrinsic 3D latent space. Specifically, we propose a novel point-to-Gaussian variational autoencoder (P2G-VAE) that encodes 3D inputs into a latent point representation, which is subsequently decoded as 3D Gaussian primitives to jointly model geometry and appearance. We then introduce a sparse point flow matching network (SPFlow) for generating the latent point representation, which simultaneously denoises the positions and features of the point latents. Our Terra enables exact multi-view consistency with native 3D representation and architecture, and supports flexible rendering from any viewpoint with only a single generation process. Furthermore, Terra achieves explorable world modeling through progressive generation in the point latent space. We conduct extensive experiments on the challenging indoor scenes from ScanNet v2. Terra achieves state-of-the-art performance in both reconstruction and generation with high 3D consistency.

翻译：世界模型因对现实世界进行全面建模而日益受到关注。然而，现有方法大多仍依赖像素对齐表示作为世界演化的基础，忽视了物理世界固有的三维本质。这可能破坏三维一致性并降低世界模型的建模效率。本文提出Terra，一种原生三维世界模型，可在本征三维潜在空间中表示并生成可探索环境。具体而言，我们提出新颖的点到高斯变分自编码器（P2G-VAE），将三维输入编码为潜在点表示，随后解码为三维高斯基元以联合建模几何与外观。我们进一步引入稀疏点流匹配网络（SPFlow）用于生成潜在点表示，该网络可同时对点潜在的位置与特征进行去噪。Terra通过原生三维表示与架构实现精确的多视角一致性，并支持仅需单次生成过程即可从任意视角进行灵活渲染。此外，Terra通过在点潜在空间中进行渐进式生成，实现了可探索的世界建模。我们在ScanNet v2的复杂室内场景数据集上进行了大量实验。Terra在重建与生成任务中均达到最先进性能，并保持高度的三维一致性。