UniQueR: Unified Query-based Feedforward 3D Reconstruction

2026-03-24

AI Summary

Existing feedforward 3D reconstruction methods are largely confined to 2.5D representations of visible surfaces, struggling to efficiently recover complete geometric structures—particularly in occluded regions. This work proposes a unified feedforward framework based on sparse 3D queries, formulating 3D reconstruction for the first time as a sparse query inference problem. By introducing explicit geometric anchors as queries in global 3D space, the method leverages a decoupled cross-attention mechanism to enable multi-view feature interaction and employs differentiable rendering with 3D Gaussians. The approach reconstructs complete scenes—including occluded areas—in a single forward pass, significantly outperforming existing feedforward methods on the Mip-NeRF 360 and VR-NeRF datasets with orders-of-magnitude fewer primitives while achieving state-of-the-art results in both rendering quality and geometric accuracy.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Feature Construction/Reformulation

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• 3D reconstruction
• occluded regions
• feedforward models
• geometric accuracy
• unposed images

Innovation

• query-based 3D reconstruction
• feedforward 3D modeling
• sparse 3D queries
• global 3D representation
• differentiable rendering