FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views
Journal:
arXiv
Published Date:
Feb 17, 2025
Abstract
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/
