Point Cloud Pre-training with Natural 3D Structures

Ryosuke Yamada, Hirokatsu Kataoka, Naoya Chiba, Yukiyasu Domae, Tetsuya Ogata

研究成果: Conference contribution

16 被引用数 (Scopus)


The construction of 3D point cloud datasets requires a great deal of human effort. Therefore, constructing a large-scale 3D point clouds dataset is difficult. In order to rem-edy this issue, we propose a newly developed point cloud fractal database (PC-FractalDB), which is a novel family of formula-driven supervised learning inspired by fractal geometry encountered in natural 3D structures. Our re-search is based on the hypothesis that we could learn rep-resentations from more real-world 3D patterns than con-ventional 3D datasets by learning fractal geometry. We show how the PC-FractalDB facilitates solving several re-cent dataset-related problems in 3D scene understanding, such as 3D model collection and labor-intensive annotation. The experimental section shows how we achieved the performance rate of up to 61.9% and 59.0% for the Scan-NetV2 and SUN RGB-D datasets, respectively, over the current highest scores obtained with the PointContrast, con-trastive scene contexts (CSC), and RandomRooms. More-over, the PC-FractalDB pre-trained model is especially ef-fective in training with limited data. For example, in 10% of training data on ScanNetV2, the PC-FractalDB pre-trained VoteNet performs at 38.3%, which is +14.8% higher accu-racy than CSC. Of particular note, we found that the pro-posed method achieves the highest results for 3D object de-tection pre-training in limited point cloud data. 11Dataset release: https://ryosuke-yamada.github.io/PointCloud-FractalDataBase/

ホスト出版物のタイトルProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
出版社IEEE Computer Society
出版ステータスPublished - 2022
イベント2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States
継続期間: 2022 6月 192022 6月 24


名前Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition


Conference2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
国/地域United States
CityNew Orleans

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識


「Point Cloud Pre-training with Natural 3D Structures」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。