ID-Pose: Sparse-view Camera Pose Estimation by Inverting Diffusion Models

• URL: http://arxiv.org/abs/2306.17140v2
• Date: Thu, 30 Nov 2023 18:33:12 GMT
• Title: ID-Pose: Sparse-view Camera Pose Estimation by Inverting Diffusion Models
• Authors: Weihao Cheng, Yan-Pei Cao, Ying Shan
• Abstract summary: We present ID-Pose which inverses the denoising diffusion process to estimate the relative pose given two input images. We extend ID-Pose to handle more than two images and estimate each pose with multiple image pairs from triangular relations. Results demonstrate that ID-Pose significantly outperforms state-of-the-art methods.
• Score: 43.86792681109704
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given sparse views of a 3D object, estimating their camera poses is a long-standing and intractable problem. Toward this goal, we consider harnessing the pre-trained diffusion model of novel views conditioned on viewpoints (Zero-1-to-3). We present ID-Pose which inverses the denoising diffusion process to estimate the relative pose given two input images. ID-Pose adds a noise to one image, and predicts the noise conditioned on the other image and a hypothesis of the relative pose. The prediction error is used as the minimization objective to find the optimal pose with the gradient descent method. We extend ID-Pose to handle more than two images and estimate each pose with multiple image pairs from triangular relations. ID-Pose requires no training and generalizes to open-world images. We conduct extensive experiments using casually captured photos and rendered images with random viewpoints. The results demonstrate that ID-Pose significantly outperforms state-of-the-art methods.

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