Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives

• URL: http://arxiv.org/abs/2205.12627v1
• Date: Wed, 25 May 2022 10:07:07 GMT
• Title: Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives
• Authors: Xinke Li, Henghui Ding, Zekun Tong, Yuwei Wu, Yeow Meng Chee
• Abstract summary: We propose a cost-effective method for automatically generating a large amount of 3D objects with annotations. These objects are auto-annotated with part labels originating from primitives. Considering the large overhead of learning on the generated dataset, we propose a dataset distillation strategy.
• Score: 44.03149443379618
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerous advancements in deep learning can be attributed to the access to large-scale and well-annotated datasets. However, such a dataset is prohibitively expensive in 3D computer vision due to the substantial collection cost. To alleviate this issue, we propose a cost-effective method for automatically generating a large amount of 3D objects with annotations. In particular, we synthesize objects simply by assembling multiple random primitives. These objects are thus auto-annotated with part labels originating from primitives. This allows us to perform multi-task learning by combining the supervised segmentation with unsupervised reconstruction. Considering the large overhead of learning on the generated dataset, we further propose a dataset distillation strategy to remove redundant samples regarding a target dataset. We conduct extensive experiments for the downstream tasks of 3D object classification. The results indicate that our dataset, together with multi-task pretraining on its annotations, achieves the best performance compared to other commonly used datasets. Further study suggests that our strategy can improve the model performance by pretraining and fine-tuning scheme, especially for the dataset with a small scale. In addition, pretraining with the proposed dataset distillation method can save 86\% of the pretraining time with negligible performance degradation. We expect that our attempt provides a new data-centric perspective for training 3D deep models.

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