用高斯点云重构图像，让自监督模型同时懂语义和空间位置。
Gaussian Masked Autoencoders
- 在像素重建基础上引入3D高斯表示，通过点绘制生成图像。
- 零样本实现前景分割、图层识别等空间理解任务，性能接近有监督方法。
- 首次将高斯基元用于非优化场景的视觉表征学习，适合新视觉模型研究者。
本文探索了将高斯点绘（Gaussian Splatting）与掩码自编码器（Masked Autoencoders, MAE）结合的方法。尽管像MAE这样的重建式自监督学习框架能学习良好的语义抽象，但缺乏显式的空间感知能力。为此，我们提出高斯掩码自编码器（Gaussian Masked Autoencoder, GMAE），旨在联合学习语义抽象与空间理解。与MAE类似，GMAE在像素空间端到端重建图像，但进一步引入基于3D高斯的中间表示，并通过点绘制渲染图像。实验表明，GMAE可实现多种零样本空间理解能力（如前景-背景分割、图像分层、边缘检测等），同时保持了来自MAE的高质量自监督语义表征。据我们所知，这是首个将高斯基元应用于非优化型单场景重建之外的图像表征学习框架。我们认为GMAE将激发该方向的进一步研究，推动高保真视觉数据建模技术的发展。更多细节见 https://brjathu.github.io/gmae。
原文摘要 · Abstract (English)
This paper explores Masked Autoencoders (MAE) with Gaussian Splatting. While reconstructive self-supervised learning frameworks such as MAE learns good semantic abstractions, it is not trained for explicit spatial awareness. Our approach, named Gaussian Masked Autoencoder, or GMAE, aims to learn semantic abstractions and spatial understanding jointly. Like MAE, it reconstructs the image end-to-end in the pixel space, but beyond MAE, it also introduces an intermediate, 3D Gaussian-based representation and renders images via splatting. We show that GMAE can enable various zero-shot learning capabilities of spatial understanding (e.g., figure-ground segmentation, image layering, edge detection, etc.) while preserving the high-level semantics of self-supervised representation quality from MAE. To our knowledge, we are the first to employ Gaussian primitives in an image representation learning framework beyond optimization-based single-scene reconstructions. We believe GMAE will inspire further research in this direction and contribute to developing next-generation techniques for modeling high-fidelity visual data. More details at https://brjathu.github.io/gmae
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