Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Multi-view 3D reconstruction remains a core challenge in computer vision,
particularly in applications requiring accurate and scalable representations
across diverse perspectives. Current leading methods such as DUSt3R employ a
fundamentally pairwise approach, processing images in pairs and necessitating
costly global alignment procedures to reconstruct from multiple views. In this
work, we propose Fast 3D Reconstruction (Fast3R), a novel multi-view
generalization to DUSt3R that achieves efficient and scalable 3D reconstruction
by processing many views in parallel. Fast3R's Transformer-based architecture
forwards N images in a single forward pass, bypassing the need for iterative
alignment. Through extensive experiments on camera pose estimation and 3D
reconstruction, Fast3R demonstrates state-of-the-art performance, with
significant improvements in inference speed and reduced error accumulation.
These results establish Fast3R as a robust alternative for multi-view
applications, offering enhanced scalability without compromising reconstruction
accuracy.
6792f5f6dc641d1a72b00736
Jianing Yang
claimed_verified
2025-01-24T09:08:08.128Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/LsDQPftQrnp_Gvanzim4J.jpeg
Jed Yang
jedyang97
user
jedyang97
6792f5f6dc641d1a72b00737
Alexander Sax
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2025-11-24T08:01:13.350Z
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ssax
saxsasha
user
saxsasha
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Kevin J. Liang
6792f5f6dc641d1a72b00739
Mikael Henaff
6792f5f6dc641d1a72b0073a
Hao Tang
6792f5f6dc641d1a72b0073b
Ang Cao
6792f5f6dc641d1a72b0073c
Joyce Chai
6792f5f6dc641d1a72b0073d
Franziska Meier
6792f5f6dc641d1a72b0073e
Matt Feiszli
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Multi-view 3D reconstruction remains a core challenge in computer vision,
particularly in applications requiring accurate and scalable representations
across diverse perspectives. Current leading methods such as DUSt3R employ a
fundamentally pairwise approach, processing images in pairs and necessitating
costly global alignment procedures to reconstruct from multiple views. In this
work, we propose Fast 3D Reconstruction (Fast3R), a novel multi-view
generalization to DUSt3R that achieves efficient and scalable 3D reconstruction
by processing many views in parallel. Fast3R's Transformer-based architecture
forwards N images in a single forward pass, bypassing the need for iterative
alignment. Through extensive experiments on camera pose estimation and 3D
reconstruction, Fast3R demonstrates state-of-the-art performance, with
significant improvements in inference speed and reduced error accumulation.
These results establish Fast3R as a robust alternative for multi-view
applications, offering enhanced scalability without compromising reconstruction
accuracy.
Fast3R, a Transformer-based extension of DUSt3R, achieves efficient and scalable multi-view 3D reconstruction by processing multiple images in parallel, improving inference speed and accuracy.
multi-view 3D reconstruction DUSt3R Transformer-based architecture camera pose estimation