← 研踪 PaperTrailCVF · CVPR 2026:cvf:725c05cd0d7bbd15f0098febb783c51c · 记录 #1
More Natural, More Real: Object-aware Gaussian Splatting for 3D Visual Decoding from Human Brain
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 00:55
摘要
Exploring human visual perception and understanding of the stereoscopic world represents a significant topic in computational neuroscience. Recent studies have provided rich Brain-3D datasets, conducted preliminary explorations into 3D visual reconstruction. However, existing research struggles to capture the differences in dynamic changes of 3D stimulus views, and there remains room for improvement in high-fidelity reconstruction and rendering. 3D Gaussian Splatting (3DGS) has recently achieved significant progress in stereoscopic view synthesis. Inspired by it, we propose BrainGS -- an innovative framework for decoding more realistic 3D objects from the brain. BrainGS incorporates a Fusion Time-Spatial Network to achieve comprehensive encoding of the brain, combined with the Multi-Attribute Controller (MAC), it decouples features using visual, semantic, and color as anchors, effectively learning the feature distribution of Brain-3D and providing initial control for 3DGS. The Multi-View Stabilizer (MVS) overcomes the challenge of capturing multi-view changes of 3D objects, creating more robust viewpoint representations. Comprehensive experiments and discussions on fMRI/EEG show the SOTA performance (2.936 FPD, 0.202 LPIPS) of BrainGS, providing reliable neural interpretations, offering new insights into brain stereovision understanding.
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记录 #1 · 2026 · 最新观测版本
More Natural, More Real: Object-aware Gaussian Splatting for 3D Visual Decoding from Human Brain
Exploring human visual perception and understanding of the stereoscopic world represents a significant topic in computational neuroscience. Recent studies have provided rich Brain-3D datasets, conducted preliminary explorations into 3D visual reconstruction. However, existing research struggles to capture the differences in dynamic changes of 3D stimulus views, and there remains room for improvement in high-fidelity reconstruction and rendering. 3D Gaussian Splatting (3DGS) has recently achieved significant progress in stereoscopic view synthesis. Inspired by it, we propose BrainGS -- an innovative framework for decoding more realistic 3D objects from the brain. BrainGS incorporates a Fusion Time-Spatial Network to achieve comprehensive encoding of the brain, combined with the Multi-Attribute Controller (MAC), it decouples features using visual, semantic, and color as anchors, effectively learning the feature distribution of Brain-3D and providing initial control for 3DGS. The Multi-View Stabilizer (MVS) overcomes the challenge of capturing multi-view changes of 3D objects, creating more robust viewpoint representations. Comprehensive experiments and discussions on fMRI/EEG show the SOTA performance (2.936 FPD, 0.202 LPIPS) of BrainGS, providing reliable neural interpretations, offering new insights into brain stereovision understanding.
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