FastMap: Revisiting Structure from Motion through First-Order Optimization

Jiahao Li; Haochen Wang; Muhammad Zubair Irshad; Igor Vasiljevic; Matthew R. Walter; Vitor Campagnolo Guizilini; Greg Shakhnarovich

arXiv · 2505.04612

FastMap: Revisiting Structure from Motion through First-Order Optimization

Jiahao Li·Haochen Wang·Muhammad Zubair Irshad·Igor Vasiljevic·Matthew R. Walter·Vitor Campagnolo Guizilini·Greg Shakhnarovich

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Abstract

We propose FastMap, a new global structure from motion method focused on speed and simplicity. Previous methods like COLMAP and GLOMAP are able to estimate high-precision camera poses, but suffer from poor scalability when the number of matched keypoint pairs becomes large, mainly due to the time-consuming process of second-order Gauss-Newton optimization. Instead, we design our method solely based on first-order optimizers. To obtain maximal speedup, we identify and eliminate two key performance bottlenecks: computational complexity and the kernel implementation of each optimization step. Through extensive experiments, we show that FastMap is up to 10 times faster than COLMAP and GLOMAP with GPU acceleration and achieves comparable pose accuracy.

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Jiahao Li, Haochen Wang, Muhammad Zubair Irshad, Igor Vasiljevic, Matthew R. Walter, Vitor Campagnolo Guizilini, Greg Shakhnarovich. 2025-05-07. FastMap: Revisiting Structure from Motion through First-Order Optimization. https://arxiv.org/abs/2505.04612

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