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 this https URL

基于点云的场景表面的准确和简明表示的学习仍然是 3D 表示学习中的一个挑战。为了解决这些限制，我们提出了一种名为 Proximity Attention Point Rendering (PAPR) 的新方法，该方法由一个基于点的场景表示和一个可微分渲染器组成。