Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Authors: name: Jianing Yang, name: Alexander Sax, name: Kevin J. Liang, name: Mikael Henaff, name: Hao Tang, name: Ang Cao, name: Joyce Chai, name: Franziska Meier, name: Matt Feiszli

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.