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Abstract
- Given sparse views of an object, estimating their camera poses is a long-standing and intractable
- problem. We harness 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 on one image, and predicts the noise
- conditioned on the other image and a decision variable for the pose. The prediction error is used as
- the objective to find the optimal pose with the gradient descent method. ID-Pose can handle more
- than two images and estimate each of the poses with multiple image pairs from triangular
- relationships. ID-Pose requires no training and generalizes to real-world images. We conduct
- experiments using high-quality real-scanned 3D objects, where ID-Pose significantly outperforms
- state-of-the-art methods.
+ 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.
