UniQueR: Unified Query-based Feedforward 3D Reconstruction

• URL: http://arxiv.org/abs/2603.22851v1
• Date: Tue, 24 Mar 2026 06:42:02 GMT
• Title: UniQueR: Unified Query-based Feedforward 3D Reconstruction
• Abstract summary: We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images.<n>Our model learns a compact set of 3D anchor points that act as explicit geometric queries.<n>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.
• Score: 68.01984215138098
• License: http://creativecommons.org/licenses/by/4.0/
• 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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