PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction

Published: 16 Jan 2024, Last Modified: 14 Mar 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: Pose estimation, NeRF, 3D Reconstruction, Transformer
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Abstract: 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 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:
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Primary Area: representation learning for computer vision, audio, language, and other modalities
Submission Number: 750