PCT-Prompt：用于点云密集预测任务的提示引导Transformer框架

中文摘要 AI 辅助

标准Transformer已被证明在点云物体分类中有效，但它们在复杂场景下的密集预测任务中的性能常因先验假设薄弱而受限。为应对这一挑战，我们提出PCT-Prompt，一种通过引入提示引导特征分支来增强标准Transformer以提升密集预测任务性能的新框架。标准Transformer分支利用预训练模型从点云数据中提取全局特征，作为处理高级特征的主干。同时，提示引导特征分支包含两个关键组件：一是细粒度特征提取块，它使用几何敏感抽象层捕获多尺度几何特征，结合PnP-3D层将局部上下文与全局正则化相融合；二是提示优化特征学习块，它生成提示token，随后通过交叉注意力机制进行优化。此外，我们引入提示丢弃机制，该机制会在Transformer各层中逐步移除提示信息，以平衡局部细节与全局一致性。在ShapeNetPart、S3DIS和DALES数据集上的实验结果表明，PCT-Prompt显著提升了标准Transformer对密集预测任务的适应性，在真实场景中实现了优异性能。

英文摘要

Standard Transformers have proven effective in point cloud object classification, but their performance in dense prediction tasks within complex scenes is often hindered by weak prior assumptions. To address this challenge, we propose PCT-Prompt, a novel framework that enhances standard Transformers by introducing a prompt-guided feature branch to improve performance in dense prediction tasks. The standard Transformer branch leverages pre-trained models for global feature extraction from point cloud data, serving as the backbone for processing high-level features. Meanwhile, the prompt-guided feature branch consists of two key components: a fine-grained feature extraction block that captures multi-scale geometric features using geometry-sensitive abstraction layer, along with the PnP-3D layer to integrate local context with global regularization. The second component, the prompt-refined feature learning block generates prompt tokens, which are subsequently refined through cross-attention mechanisms. Additionally, we introduce a prompt drop mechanism that progressively removes prompt information across Transformer layers, balancing local details and global consistency. Experimental results on the ShapeNetPart, S3DIS, and DALES datasets demonstrate that PCT-Prompt significantly improves the adaptability of standard Transformers to dense prediction tasks, achieving strong performance in real-world scenarios.

发表机构

• China University of Geosciences（中国地质大学）
• Li Auto Inc（理想汽车）

机构由 AI 辅助整理，请以论文原文为准。