Mind2Cloud: EEG-to-Point Cloud Generation with Two-Granularity Diffusion Decoding

Reconstructing 3D objects from brain signals offers a promis-ing avenue for understanding human visual cognition. While prior workhas shown initial success using EEG signals for 3D reconstruction, ex-isting methods typically employ a uniform diffusion decoder, overlook-ing the evolving semantic granularity of both EEG representations andthe diffusion denoising process. In this paper, we propose Mind2Cloud, anovel EEG-to-point-cloud generation framework based on two-granularitydiffusion decoding. The core of Mind2Cloud is a time-aware decoder thatintegrates a global Transformer branch and a local Point-Voxel CNN(PVCNN) branch across diffusion timesteps through a learnable fusionmask. Specifically, Transformer layers are incorporated into the earlyupsampling stages to capture global object structure under high uncer-tainty, while PVCNN modules are used in later stages to refine local ge-ometric details. Inspired by the hierarchical nature of EEG-based visualrepresentations, this design dynamically adapts its spatial granularity inaccordance with the coarse-to-fine trajectory of diffusion denoising. Wefurther introduce an adversarial refinement module to enhance geometricrealism and semantic consistency. Extensive experiments on the EEG-3Ddataset across all 12 subjects demonstrate that Mind2Cloud outperformsprior work in both geometric accuracy and semantic alignment, settinga new benchmark for EEG-to-point-cloud generation. Our source code isavailable at https://github.com/duasoi/Mind2Cloud.