Learning 3D Representations from Procedural 3D Programs
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.
6746871ae2e2e857eac6df53
Xuweiyi Chen
6746871ae2e2e857eac6df54
Zezhou Cheng
Learning 3D Representations from Procedural 3D Programs
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.
Self-supervised learning of 3D representations from procedurally generated 3D programs achieves performance comparable to state-of-the-art methods on various 3D tasks, focusing on geometric structures rather than semantic content.
self-supervised learning 3D representations 3D point clouds procedural 3D programs shape classification part segmentation masked point cloud completion geometric structures