Reliev3R：让前馈三维重建摆脱多视图几何标注的依赖
Reliev3R: Relieving Feed-forward 3D Reconstruction from Multi-View Geometric Annotations
用预训练单目模型的零样本相对深度与稀疏对应替代多视图几何标注，配合歧义感知相对深度损失和基于三角学的重投影损失，为前馈重建模型提供可扩展的低成本弱监督训练方案。
原文依据 we design an ambiguity-aware relative depth loss and a trigonometry-based reprojection loss
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论随着近期进展，前馈重建模型（FFRM）在重建质量和对多种下游任务的适应性方面展现出巨大潜力。然而，对多视图几何标注（如三维点图和相机位姿）的过度依赖，使 FFRM 的全监督训练方案难以扩展。本文提出 Reliev3R，一种从零开始训练 FFRM 的弱监督范式，无需成本高昂的多视图几何标注。该方法摆脱了对几何传感数据和计算量巨大的运动恢复结构预处理的依赖，直接从预训练模型零样本预测给出的单目相对深度和图像稀疏对应关系中获取三维知识。Reliev3R 的核心在于设计了歧义感知相对深度损失和基于三角学的重投影损失，以促进对多视图几何一致性的监督。在数据更少的条件下从零开始训练，Reliev3R 追平了其全监督同类模型，朝着低成本三维重建监督和可扩展 FFRM 迈出了一步。
英文原文摘要 1137 字符
With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However, the excessive reliance on multi-view geometric annotations, e.g. 3D point maps and camera poses, makes the fully-supervised training scheme of FFRMs difficult to scale up.In this paper, we propose Reliev3R, a weakly-supervised paradigm for training FFRMs from scratch without cost-prohibitive multi-view geometric annotations. Relieving the reliance on geometric sensory data and compute-exhaustive structure-from-motion preprocessing, our method draws 3D knowledge directly from monocular relative depths and image sparse correspondences given by zero-shot predictions of pretrained models.At the core of Reliev3R, we design an ambiguity-aware relative depth loss and a trigonometry-based reprojection loss to facilitate supervision for multi-view geometric consistency.Training from scratch with the less data, Reliev3R catches up with its fully-supervised sibling models, taking a step towards low-cost 3D reconstruction supervisions and scalable FFRMs.