Learning 3D Representations from Procedural 3D Programs
Authors: name: Xuweiyi Chen, name: Zezhou Cheng

Self-supervised learning has emerged as a promising approach for acquiring
transferable 3D representations from unlabeled 3D point clouds. Unlike 2D
images, which are widely accessible, acquiring 3D assets requires specialized
expertise or professional 3D scanning equipment, making it difficult to scale
and raising copyright concerns. To address these challenges, we propose
learning 3D representations from procedural 3D programs that automatically
generate 3D shapes using simple primitives and augmentations.
  Remarkably, despite lacking semantic content, the 3D representations learned
from this synthesized dataset perform on par with state-of-the-art
representations learned from semantically recognizable 3D models (e.g.,
airplanes) across various downstream 3D tasks, including shape classification,
part segmentation, and masked point cloud completion. Our analysis further
suggests that current self-supervised learning methods primarily capture
geometric structures rather than high-level semantics.