3D object reconstruction using a few views despite noisy camera poses with FvOR
3D object reconstruction using a few views despite noisy camera poses with FvOR
FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction
arXiv paper abstract https://arxiv.org/abs/2205.07763
arXiv PDF paper https://arxiv.org/pdf/2205.07763.pdf
GitHub https://github.com/zhenpeiyang/FvOR
Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in computer vision.
State-of-the-art approaches typically assume accurate camera poses as input, which could be difficult to obtain in realistic settings.
… present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses.
… approach is a fast and robust multi-view reconstruction algorithm to jointly refine 3D geometry and camera pose estimation using learnable neural network modules.
… provide a thorough benchmark of state-of-the-art approaches for this problem on ShapeNet.
… achieves best-in-class results … two orders of magnitude faster than the recent optimization-based approach IDR. …
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