Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives

TL;DR

Primitive3D introduces a cost-effective method for synthesizing large-scale 3D object datasets from randomly assembled primitives, enabling improved 3D classification performance and efficient pretraining strategies.

Contribution

The paper presents a novel primitive-based dataset synthesis method combined with dataset distillation, enhancing 3D model training efficiency and accuracy.

Findings

Achieves state-of-the-art performance in 3D classification tasks.

Pretraining with the synthesized dataset improves model accuracy.

Dataset distillation reduces pretraining time by 86% with minimal performance loss.

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.