Get 3D shape of object by neural reconstruction that models photon-particle interaction with ReTR
Get 3D shape of object by neural reconstruction that models photon-particle interaction with ReTR
Rethinking Rendering in Generalizable Neural Surface Reconstruction: A Learning-based Solution
arXiv paper abstract https://arxiv.org/abs/2305.18832
arXiv PDF paper https://arxiv.org/pdf/2305.18832.pdf
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.
... 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.
... method outperforms the current state-of-the-art approaches in terms of reconstruction quality and generalization ability.
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