FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views
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Abstract (arXiv)
We present FLARE, a feed-forward model designed to infer high-quality camera poses and 3D geometry from uncalibrated sparse-view images (i.e., as few as 2-8 inputs), which is a challenging yet practical setting in real-world applications. Our solution features a cascaded learning paradigm with camera pose serving as the critical bridge, recognizing its essential role in mapping 3D structures onto 2D image planes. Concretely, FLARE starts with camera pose estimation, whose results condition the subsequent learning of geometric structure and appearance, optimized through the objectives of geometry reconstruction and novel-view synthesis. Utilizing large-scale public datasets for training, our method delivers state-of-the-art performance in the tasks of pose estimation, geometry reconstruction, and novel view synthesis, while maintaining the inference efficiency (i.e., less than 0.5 seconds). The project page and code can be found at: https://zhanghe3z.github.io/FLARE/
Key facts. Cited by 180 papers in the map (data through 14 Oct 2026). Builds on 75 of its 99 references that are in the map. First posted on arXiv 17 Feb 2025.
Builds on
75 of 99 references.
References · 75
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50 most cited of 75 All 75 (JSON)
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Citing papers per year: 2025: 81; 2026: 99.
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