Given a set of calibrated images of a scene, we present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. While many approaches focus on recovering high-fidelity 3D scenes, we focus on parsing a scene into mid-level 3D representations made of a small set of textured primitives. Such representations are interpretable, easy to manipulate and suited for physics-based simulations. Moreover, unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images through differentiable rendering. Specifically, we model primitives as textured superquadric meshes and optimize their parameters from scratch with an image rendering loss. We highlight the importance of modeling transparency for each primitive, which is critical for optimization and also enables handling varying numbers of primitives. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points, while providing amodal shape completions of unseen object regions. We compare our approach to the state of the art on diverse scenes from DTU, and demonstrate its robustness on real-life captures from BlendedMVS and Nerfstudio. We also showcase how our results can be used to effortlessly edit a scene or perform physical simulations. Code and video results are available at https://www.tmonnier.com/DBW .

翻译：给定一组场景的标定图像，我们提出了一种通过三维基元生成简单、紧凑且可操作的三维世界表示的方法。诸多方法致力于重建高保真度三维场景，而我们的重点在于将场景解析为由少量纹理基元组成的中等层次三维表示。这种表示具有可解释性、易于操作，并适用于基于物理的模拟。此外，与依赖三维输入数据的现有基元分解方法不同，我们的方法通过可微分渲染直接操作图像。具体而言，我们将基元建模为纹理超二次曲面网格，并通过图像渲染损失从头优化其参数。我们强调了为每个基元建模透明度的重要性——这对优化至关重要，同时也能处理可变数量的基元。结果表明，生成的纹理基元能够忠实重建输入图像，精确建模可见三维点，并对未见物体区域实现模态形状补全。我们在DTU数据集的多场景上对比了当前最优方法，并在BlendedMVS和Nerfstudio的真实场景捕捉中验证了其鲁棒性。我们还展示了如何将结果用于轻松编辑场景或进行物理模拟。代码与视频结果详见 https://www.tmonnier.com/DBW。