用稀疏3D查询实现高效精准的无姿态图像三维重建
UniQueR: Unified Query-based Feedforward 3D Reconstruction
- 将三维重建转化为稀疏3D查询推理问题，直接在全局空间建模几何
- 仅用极少点元（少一个数量级）即达顶尖渲染与几何精度
- 适合追求高效高保真3D重建的研究者与工业应用
我们提出UniQueR，一种统一的基于查询的前馈式3D重建框架，用于从无姿态图像中高效且准确地重建三维场景。现有前馈模型如DUSt3R、VGGT和AnySplat通常生成像素级点图或像素对齐的高斯分布，本质上仍为2.5D，仅限于可见表面。相比之下，UniQueR将重建问题形式化为稀疏3D查询推理。模型学习一组紧凑的3D锚点作为显式几何查询，使网络能在单次前向传播中推断出包括遮挡区域在内的完整场景结构。每个查询在全局3D空间中编码空间与外观先验，并生成一组可微渲染的3D高斯。通过跨多视角特征的统一查询交互与解耦交叉注意力设计，UniQueR在显著降低内存与计算开销的同时，实现了强大的几何表达能力。在Mip-NeRF 360和VR-NeRF上的实验表明，UniQueR在渲染质量与几何准确性上均超越现有前沿方法，使用的点元数量比密集型方案少一个数量级。
原文摘要 · Abstract (English)
We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images. Existing feedforward models such as DUSt3R, VGGT, and AnySplat typically predict per-pixel point maps or pixel-aligned Gaussians, which remain fundamentally 2.5D and limited to visible surfaces. In contrast, UniQueR formulates reconstruction as a sparse 3D query inference problem. Our model learns a compact set of 3D anchor points that act as explicit geometric queries, enabling the network to infer scene structure, including geometry in occluded regions--in a single forward pass. Each query encodes spatial and appearance priors directly in global 3D space (instead of per-frame camera space) and spawns a set of 3D Gaussians for differentiable rendering. By leveraging unified query interactions across multi-view features and a decoupled cross-attention design, UniQueR achieves strong geometric expressiveness while substantially reducing memory and computational cost. Experiments on Mip-NeRF 360 and VR-NeRF demonstrate that UniQueR surpasses state-of-the-art feedforward methods in both rendering quality and geometric accuracy, using an order of magnitude fewer primitives than dense alternatives.
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