Generalizable neural surface reconstruction techniques have attracted great attention in recent years. However, they encounter limitations of low confidence depth distribution and inaccurate surface reasoning due to the oversimplified volume rendering process employed. In this paper, we present Reconstruction TRansformer (ReTR), a novel framework that leverages the transformer architecture to redesign the rendering process, enabling complex photon-particle interaction modeling. It introduces a learnable meta-ray token and utilizes the cross-attention mechanism to simulate the interaction of photons with sampled points and render the observed color. Meanwhile, by operating within a high-dimensional feature space rather than the color space, ReTR mitigates sensitivity to projected colors in source views. Such improvements result in accurate surface assessment with high confidence. We demonstrate the effectiveness of our approach on various datasets, showcasing how our method outperforms the current state-of-the-art approaches in terms of reconstruction quality and generalization ability.

本研究介绍了一种利用 Transformer 架构将重建过程重新设计的新框架，名为 Reconstruction TRansformer（ReTR），其增强了光子与表面交互建模，同时通过在高维特征空间操作（而非颜色空间），缓解了源视角中投影颜色的敏感性，从而实现了高精度、高置信度的表面评估。

可推广神经表面重建中的渲染思考：基于学习的解决方案