PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction
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Cited by 31 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images even with little visual overlap, while simultaneously estimating the relative camera poses in ~1.3 seconds on a single A100 GPU. PF-LRM is a highly scalable method utilizing the self-attention blocks to exchange information between 3D object tokens and 2D image tokens; we predict a coarse point cloud for each view, and then use a differentiable Perspective-n-Point (PnP) solver to obtain camera poses. When trained on a huge amount of multi-view posed data of ~1M objects, PF-LRM shows strong cross-dataset generalization ability, and outperforms baseline methods by a large margin in terms of pose prediction accuracy and 3D reconstruction quality on various unseen evaluation datasets. We also demonstrate our model's applicability in downstream text/image-to-3D task with fast feed-forward inference. Our project website is at: https://totoro97.github.io/pf-lrm .
Key facts. Cited by 165 papers in the map (data through 14 Oct 2026). Builds on 61 of its 77 references that are in the map. First posted on arXiv 20 Nov 2023.
Builds on
61 of 77 references.
References · 61
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- 2023RelPose++: Recovering 6D Poses from Sparse-view Observationscited by 87
- 2023EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimationcited by 79
50 most cited of 61 All 61 (JSON)
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Citing papers per year: 2023: 2; 2024: 79; 2025: 59; 2026: 25.
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