@book {5289,
	title = {Lecture Notes in Computer ScienceComputer Vision {\textendash} ECCV 2022Image2Point: 3D Point-Cloud Understanding with~2D Image Pretrained Models},
	volume = {13697},
	year = {2022},
	month = {10/2022},
	pages = {638 - 656},
	publisher = {Springer Nature Switzerland},
	organization = {Springer Nature Switzerland},
	address = {Cham},
	abstract = {<p>3D point-clouds and 2D images are different visual representations of the physical world. While human vision can understand both representations, computer vision models designed for 2D image and 3D point-cloud understanding are quite different. Our paper explores the potential of transferring 2D model architectures and weights to understand 3D point-clouds, by empirically investigating the feasibility of the transfer, the benefits of the transfer, and shedding light on why the transfer works. We discover that we can indeed use the same architecture and pretrained weights of a neural net model to understand both images and point-clouds. Specifically, we transfer the image-pretrained model to a point-cloud model by copying or inflating the weights. We find that finetuning the transformed image-pretrained models (FIP) with minimal efforts{\textemdash}only on input, output, and normalization layers{\textemdash}can achieve competitive performance on 3D point-cloud classification, beating a wide range of point-cloud models that adopt task-specific architectures and use a variety of tricks. When finetuning the whole model, the performance gets further improved. Meanwhile, FIP improves data efficiency, reaching up to 10.0 top-1 accuracy percent on few-shot classification. It also speeds up the training of point-cloud models by up to 11.1x for a target accuracy (e.g., 90\% accuracy). Lastly, we provide an explanation of the image to point-cloud transfer from the aspect of neural collapse. The code is available at: https://github.com/chenfengxu714/image2point.</p>
},
	isbn = {978-3-031-19835-9},
	issn = {0302-9743},
	doi = {10.1007/978-3-031-19836-610.1007/978-3-031-19836-6_36},
	url = {https://link.springer.com/10.1007/978-3-031-19836-6},
	author = {Xu, Chenfeng and Yang, Shijia and Galanti, Tomer and Wu, Bichen and Yue, Xiangyu and Zhai, Bohan and Zhan, Wei and Vajda, Peter and Keutzer, Kurt and Tomizuka, Masayoshi},
	editor = {Avidan, Shai and Brostow, Gabriel and Ciss{\'e}, Moustapha and Farinella, Giovanni Maria and Hassner, Tal}
}
