Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass

• URL: http://arxiv.org/abs/2501.13928v1
• Date: Thu, 23 Jan 2025 18:59:55 GMT
• Title: Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
• Authors: Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, Matt Feiszli,
• Abstract summary: 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.<n>Fast3R demonstrates state-of-the-art performance, with significant improvements in inference speed and reduced error accumulation.
• Score: 68.78222900840132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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