從程序化3D程式學習3D表示

Learning 3D Representations from Procedural 3D Programs

November 25, 2024

作者: Xuweiyi Chen, Zezhou Cheng

cs.AI

摘要

自我監督學習已成為一種有前途的方法，用於從未標記的3D點雲中獲取可轉移的表示。與廣泛可取得的2D圖像不同，獲取3D資產需要專業知識或專業的3D掃描設備，這使得擴展變得困難並引起版權問題。為了應對這些挑戰，我們提出從程序化3D程序中學習3D表示，該程序可以自動生成使用簡單基元和增強技術的3D形狀。 值得注意的是，盡管缺乏語義內容，從這個合成數據集中學習的3D表示在各種下游3D任務（包括形狀分類、部分分割和遮罩點雲完成）中表現與從語義可識別的3D模型（例如飛機）中學習的最先進表示相當。我們的分析進一步表明，當前的自我監督學習方法主要捕捉幾何結構而不是高層次語義。

English

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.