  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,
influence 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: zero-shot 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/.
