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
646afdd535c7a57f936d3ff5
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/LsDQPftQrnp_Gvanzim4J.jpeg
Jed Yang
user
jedyang97
6792f5f6dc641d1a72b00737
Alexander Sax
6792f5f6dc641d1a72b00738
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