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

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arxiv 2306.17140 v2 pith:E4VNKRQR submitted 2023-06-29 cs.CV

Weihao Cheng , Yan-Pei Cao , Ying Shan This is my paper · ORCID
classification cs.CV
keywords id-poseposeimagesdiffusionimagecameraconditionedestimate
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis

    cs.CV 2024-12 conditional novelty 6.0 of 10

    SparseAGS jointly refines initial camera poses and reconstructs 3D from sparse views using multi-view SDS diffusion priors and explicit outlier removal.

  2. Generalizable Single-view Object Pose Estimation by Two-side Generating and Matching

    cs.CV 2024-11 conditional novelty 6.0 of 10

    Two-side generation and matching of intermediate views with a score distillation loss improves single-reference object pose estimation under large viewpoint changes.

  3. Pragmatist: Multiview Conditional Diffusion Models for High-Fidelity 3D Reconstruction from Unposed Sparse Views

    cs.CV 2024-12 conditional novelty 5.0 of 10

    Pragmatist turns sparse unposed photos of an object into a high-fidelity 3D mesh by generating consistent canonical views with a diffusion model, reconstructing a triplane mesh, then refining camera poses and texture ...

  4. Sparse-View 3D Reconstruction: Recent Advances and Open Challenges

    cs.CV 2025-07 conditional novelty 3.0 of 10

    A comprehensive survey that organizes sparse-view 3D reconstruction methods into geometry-based, NeRF, 3DGS, and diffusion-based categories, with benchmarks and open challenges.

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