We propose a novel method for 3D object reconstruction from a sparse set of views captured from a 360-degree calibrated camera rig. We represent the object surface through a hybrid model that uses both an MLP-based neural representation and a triangle mesh. A key contribution in our work is a novel object-centric sampling scheme of the neural representation, where rays are shared among all views. This efficiently concentrates and reduces the number of samples used to update the neural model at each iteration. This sampling scheme relies on the mesh representation to ensure also that samples are well-distributed along its normals. The rendering is then performed efficiently by a differentiable renderer. We demonstrate that this sampling scheme results in a more effective training of the neural representation, does not require the additional supervision of segmentation masks, yields state of the art 3D reconstructions, and works with sparse views on the Google's Scanned Objects, Tank and Temples and MVMC Car datasets.

翻译：我们提出了一种新颖方法，用于从稀疏视图中进行三维物体重建，这些视图由360度标定相机阵列采集。通过结合基于多层感知机(MLP)的神经表示与三角形网格的混合模型，我们对物体表面进行表征。本研究的关键贡献在于提出了一种新颖的对象中心神经表示采样方案，其中光线在所有视图中共享。该方案能高效地集中并减少每次迭代中用于更新神经模型的样本数量。该采样方案依托网格表示确保样本沿法线方向均匀分布。随后，通过可微分渲染器高效完成渲染过程。我们证明该采样方案能更有效地训练神经表示，无需分割掩模的额外监督，可生成最先进的三维重建结果，并在Google Scanned Objects、Tank and Temples以及MVMC Car数据集上适用于稀疏视图场景。