3D Vision and Language Pretraining with Large-Scale Synthetic Data
<|reference_start|>3D Vision and Language Pretraining with Large-Scale Synthetic Data: 3D Vision-Language Pre-training (3D-VLP) aims to provide a pre-train model which can bridge 3D scenes with natural language, which is an important technique for embodied intelligence. However, current 3D-VLP datasets are hindered by limited scene-level diversity and insufficient fine-grained annotations (only 1.2K scenes and 280K textual annotations in ScanScribe), primarily due to the labor-intensive of collecting and annotating 3D scenes. To overcome these obstacles, we construct SynVL3D, a comprehensive synthetic scene-text corpus with 10K indoor scenes and 1M descriptions at object, view, and room levels, which has the advantages of diverse scene data, rich textual descriptions, multi-grained 3D-text associations, and low collection cost. Utilizing the rich annotations in SynVL3D, we pre-train a simple and unified Transformer for aligning 3D and language with multi-grained pretraining tasks. Moreover, we propose a synthetic-to-real domain adaptation in downstream task fine-tuning process to address the domain shift. Through extensive experiments, we verify the effectiveness of our model design by achieving state-of-the-art performance on downstream tasks including visual grounding, dense captioning, and question answering.<|reference_end|>
@article{yang20243d,
    title={3D Vision and Language Pretraining with Large-Scale Synthetic Data},
    author={Dejie Yang and Zhu Xu and Wentao Mo and Qingchao Chen and Siyuan Huang
  and Yang Liu},
    journal={arXiv preprint arXiv:2407.06084},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06084},
    primaryClass={cs.CV}
}