UniQueR: Unified Query-based Feedforward 3D Reconstruction

arXiv:2603.22851 · cs.CV, cs.AI · Submitted 2026-03-24 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-03-24

Updated: 2026-09-07

Comments: Project page: https://uniquer3d.github.io/

Project page: https://uniquer3d.github.io

License: http://creativecommons.org/licenses/by/4.0/

The gist: We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images.

Terminology

Abstract

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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