FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction

• URL: http://arxiv.org/abs/2205.07763v1
• Date: Mon, 16 May 2022 15:39:27 GMT
• Title: FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction
• Authors: Zhenpei Yang, Zhile Ren, Miguel Angel Bautista, Zaiwei Zhang, Qi Shan, Qixing Huang
• Abstract summary: Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in computer vision. We present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses.
• Score: 37.81077373162092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. In this paper, we present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses. The core of our approach is a fast and robust multi-view reconstruction algorithm to jointly refine 3D geometry and camera pose estimation using learnable neural network modules. We provide a thorough benchmark of state-of-the-art approaches for this problem on ShapeNet. Our approach achieves best-in-class results. It is also two orders of magnitude faster than the recent optimization-based approach IDR. Our code is released at \url{https://github.com/zhenpeiyang/FvOR/}

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