Thanks for your approval, and clarify our novelty
Thank you very much for your approval of AD-PT's generalization ability. We appreciate the reviewer's comments on our works. Due to the limited number of characters in the rebuttal phase, we would like to clarify the novelty of AD-PT from the following three aspects.
**Motivation-level**: AD-PT is the first work aiming at using totally different pre-training and fine-tuning datasets and the first work that can provide backbones with strong generalization ability. Such a new pre-training paradigm can bring the following three advantages.
- Data-efficient. Previous works mainly follow paradigms that use **the same dataset** for pre-training and fine-tuning. However, when the sensor is updated, the previous pre-training model can not improve the performance of the new data. In contrast, AD-PT can provide backbones with strong generalization ability, which means that the previous pre-trained backbone can improve the performance of new data. Meanwhile, in the rebuttal phase, we show that our method can easily combine with multi-dataset pre-training, which means we can use data from old sensors. **In conclusion, AD-PT can reuse previous data to provide a good pre-training model without re-collecting a large amount of data from the new sensor**.
- Training-efficient. Previous methods need to pre-train new backbone parameters using new data when we want to improve the performance of newly collected data, which is time-consuming. **In contrast, AD-PT can achieve a one-shot pre-training to improve the performance of multiple domains, greatly reducing pre-training time and computation power consumption**.
- Scaling up performance. Limited by the amount of data, previous works are hard to observe the performance gains when increasing the pre-training data. However, **AD-PT verifies that the downstream performance is positively correlated with the amount of the upstream pre-training data**.
**Framework-level**: AD-PT is the first work to use semi-supervised pre-training and provides a complete set of pre-training processes including data labeling and training algorithm process (We will release the large-scale pseudo-labeled point clouds, about 1M data, which is the largest-scale labeled outdoor point-cloud dataset). Meanwhile, the starting point of AD-PT is totally different from previous semi-supervised learning, and data augmentation works as follows.
- Previous pre-training methods mainly use MAE-based or contrastive learning-based methods to obtain backbone parameters to achieve the best in the single domain, while AD-PT explores a new pseudo-label-based method and totally considers the differences between upstream and downstream datasets, such as the differences in semantic classes between upstream and downstream. As a result, we propose to Unknown-aware Instance Learning Head to perform the open-set detection on the downstream dataset.
- Furthermore, we provide a complete pre-training process including data preparation and a pre-training algorithm that is totally different from the previous pre-training methods as shown in Fig. 2.
**Component-level**: Finally, each proposed module is also different from the existing ones.
- Data preparation. Based on the observation that the quality of pseudo labels can affect the performance of pre-training process. We propose to use the class-aware pseudo generator and semi-supervised learning to obtain as accurate pseudo labels as possible. Pseudo-labeling as mentioned by the reviewer improves semi-supervised performance by using pseudo-labeling by means of threshold which is only a part of our method. Besides, our re-scaling and re-sampling methods meanly consider improving the diversity of pre-training data, which is different from most previous data augmentation methods aiming at improving the performance of the single-domain model.
- Training algorithm. AD-PT is the first work that introduces the idea of open-set learning to the pre-training task, which fully considers the taxonomy difference between upstream and downstream datasets. We found some negative samples, that could be ignored during the upstream pre-training period, may be useful for different downstream fine-tuning tasks (due to the taxonomy differences between datasets). As a result, we consider such samples as undetected instances. Such an idea is different from previous MAE-based and contrastive-learning-based methods.