用线条构建3D表示，提升少视角重建精度
PlückeRF: A Line-based 3D Representation for Few-view Reconstruction
- 以结构化线条作为3D表示，关联输入图像像素射线
- 在少视角下比三平面表示和现有方法更精准
- 适合需要快速高精度3D重建的场景
前馈式3D重建方法旨在直接从输入图像预测场景的3D结构，提供比逐场景优化更快的替代方案。尽管单视图和少视图重建已利用学习先验来推断物体形状与外观，包括未观测区域，但当多视图可用时，仍有显著潜力可挖掘。为此，我们提出一种少视角重建模型，更有效地利用多视图信息。该方法引入一种简单机制，将3D表示与输入视图的像素射线相连接，实现相近3D位置之间以及3D位置与邻近像素射线之间的信息优先共享。通过定义3D表示为一组结构化、带特征的线条，即PlückeRF表示，我们在重建质量上超越了等效的三平面表示及当前最先进的前馈重建方法。
原文摘要 · Abstract (English)
Feed-forward 3D reconstruction methods aim to predict the 3D structure of a scene directly from input images, providing a faster alternative to per-scene optimization approaches. Significant progress has been made in single-view and few-view reconstruction using learned priors that infer object shape and appearance, even for unobserved regions. However, there is substantial potential to enhance these methods by better leveraging information from multiple views when available. To address this, we propose a few-view reconstruction model that more effectively harnesses multi-view information. Our approach introduces a simple mechanism that connects the 3D representation with pixel rays from the input views, allowing for preferential sharing of information between nearby 3D locations and between 3D locations and nearby pixel rays. We achieve this by defining the 3D representation as a set of structured, feature-augmented lines; the PlückeRF representation. Using this representation, we demonstrate improvements in reconstruction quality over the equivalent triplane representation and state-of-the-art feedforward reconstruction methods.
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