Abstract
With the rise of Large Language Models (LLMs) such as GPT-3, these models exhibit strong generalization capabilities. Through transfer learning techniques such as fine-tuning and prompt tuning, they can be adapted to various downstream tasks with minimal parameter adjustments. This approach is particularly common in the field of Natural Language Processing (NLP). This paper aims to explore the effectiveness of common prompt tuning methods in 3D object detection. We investigate whether a model trained on the large-scale Waymo dataset can serve as a foundation model and adapt to other scenarios within the 3D object detection field. This paper sequentially examines the impact of prompt tokens and prompt generators, and further proposes a Scene-Oriented Prompt Pool (\textbf{SOP$^2$}). We demonstrate the effectiveness of prompt pools in 3D object detection, with the goal of inspiring future researchers to delve deeper into the potential of prompts in the 3D field.
Abstract (translated)
随着大型语言模型（如GPT-3）的兴起，这些模型展现出了强大的泛化能力。通过迁移学习技术（例如微调和提示调整），它们可以在最少参数调整的情况下适应各种下游任务。这种方法在自然语言处理（NLP）领域尤为常见。本文旨在探索常见的提示调整方法在三维物体检测中的有效性。我们研究了一个基于大规模Waymo数据集训练的模型能否作为基础模型，并且能够适应其他场景内的三维物体检测任务。本文依次探讨了提示标记和提示生成器的影响，进一步提出了一种面向场景的提示池（Scene-Oriented Prompt Pool, \textbf{SOP$^2$}）。我们展示了在三维物体检测中提示池的有效性，旨在激发未来的研究者深入挖掘提示技术在三维领域的潜在价值。
URL
https://arxiv.org/abs/2512.08223
