Mind2Cloud：基于双粒度扩散解码的脑电信号到点云生成
Mind2Cloud: EEG-to-Point Cloud Generation with Two-Granularity Diffusion Decoding
- Guangdong University of Technology（广东工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有EEG到点云生成方法忽视语义粒度演变的问题，提出Mind2Cloud框架，采用双粒度扩散解码，结合全局Transformer与局部PVCNN，并引入对抗细化模块，在EEG-3D数据集上超越先前方法，提升几何与语义性能。
AI中文摘要：
从脑信号重建三维物体为理解人类视觉认知提供了一条有前景的途径。尽管先前的工作已展示了利用脑电（EEG）信号进行三维重建的初步成功，但现有方法通常采用统一的扩散解码器，忽视了EEG表征和扩散去噪过程中语义粒度的演变。本文提出了Mind2Cloud，一种基于双粒度扩散解码的新型EEG到点云生成框架。Mind2Cloud的核心是一个时间感知解码器，它通过可学习的融合掩码，在扩散时间步中整合了全局Transformer分支和局部点-体素CNN（PVCNN）分支。具体而言，Transformer层被纳入早期上采样阶段，以在高不确定性下捕获全局物体结构，而PVCNN模块则用于后期阶段以细化局部几何细节。受EEG视觉表征层次性启发，该设计根据扩散去噪的从粗到细轨迹动态调整其空间粒度。我们进一步引入了一个对抗性细化模块，以增强几何真实感和语义一致性。在EEG-3D数据集上覆盖所有12个受试者的广泛实验表明，Mind2Cloud在几何准确性和语义对齐方面均优于先前工作，为EEG到点云生成设立了新的基准。我们的源代码可在该https URL获取。
英文摘要：
Reconstructing 3D objects from brain signals offers a promising avenue for understanding human visual cognition. While prior work has shown initial success using EEG signals for 3D reconstruction, existing methods typically employ a uniform diffusion decoder, overlooking the evolving semantic granularity of both EEG representations and the diffusion denoising process. In this paper, we propose Mind2Cloud, a novel EEG-to-point-cloud generation framework based on two-granularity diffusion decoding. The core of Mind2Cloud is a time-aware decoder that integrates a global Transformer branch and a local Point-Voxel CNN (PVCNN) branch across diffusion timesteps through a learnable fusion mask. Specifically, Transformer layers are incorporated into the early upsampling stages to capture global object structure under high uncertainty, while PVCNN modules are used in later stages to refine local geometric details. Inspired by the hierarchical nature of EEG-based visual representations, this design dynamically adapts its spatial granularity in accordance with the coarse-to-fine trajectory of diffusion denoising. We further introduce an adversarial refinement module to enhance geometric realism and semantic consistency. Extensive experiments on the EEG-3D dataset across all 12 subjects demonstrate that Mind2Cloud outperforms prior work in both geometric accuracy and semantic alignment, setting a new benchmark for EEG-to-point-cloud generation. Our source code is available at https://github.com/duasoi/Mind2Cloud.
