Learning accurate and parsimonious point cloud representations of scene surfaces from scratch remains a challenge in 3D representation learning. Existing point-based methods often suffer from the vanishing gradient problem or require a large number of points to accurately model scene geometry and texture. To address these limitations, we propose Proximity Attention Point Rendering (PAPR), a novel method that consists of a point-based scene representation and a differentiable renderer. Our scene representation uses a point cloud where each point is characterized by its spatial position, foreground score, and view-independent feature vector. The renderer selects the relevant points for each ray and produces accurate colours using their associated features. PAPR effectively learns point cloud positions to represent the correct scene geometry, even when the initialization drastically differs from the target geometry. Notably, our method captures fine texture details while using only a parsimonious set of points. We also demonstrate four practical applications of our method: geometry editing, object manipulation, texture transfer, and exposure control. More results and code are available on our project website at https://zvict.github.io/papr/.

翻译：从零学习准确且精简的场景表面点云表征，仍是3D表征学习中的一个挑战。现有基于点的方法常受梯度消失问题困扰，或需要大量点才能精确建模场景几何与纹理。为解决这些局限，我们提出邻近注意力点云渲染（PAPR）——一种由基于点的场景表征和可微分渲染器组成的新方法。该场景表征采用点云，其中每个点由空间位置、前景得分和视角无关特征向量描述。渲染器为每条光线选择相关点，并利用其关联特征生成精确颜色。即便初始点云分布与目标几何形态差异显著，PAPR也能有效学习点云位置以表征正确场景几何。值得注意的是，我们的方法在仅使用精简点集的同时，仍能捕捉精细纹理细节。我们进一步展示了该方法的四种实际应用：几何编辑、物体操控、纹理迁移和曝光控制。更多结果与代码详见项目网站：https://zvict.github.io/papr/。