GeoMotion: Rethinking Motion Segmentation via Latent 4D Geometry
This addresses motion segmentation for computer vision applications, offering a more efficient and robust method compared to conventional multi-stage pipelines.
The paper tackles motion segmentation in dynamic scenes by proposing a fully learning-based approach that directly infers moving objects from latent features via attention, bypassing explicit correspondence estimation. It achieves state-of-the-art performance with high efficiency, as demonstrated in extensive experiments.
Motion segmentation in dynamic scenes is highly challenging, as conventional methods heavily rely on estimating camera poses and point correspondences from inherently noisy motion cues. Existing statistical inference or iterative optimization techniques that struggle to mitigate the cumulative errors in multi-stage pipelines often lead to limited performance or high computational cost. In contrast, we propose a fully learning-based approach that directly infers moving objects from latent feature representations via attention mechanisms, thus enabling end-to-end feed-forward motion segmentation. Our key insight is to bypass explicit correspondence estimation and instead let the model learn to implicitly disentangle object and camera motion. Supported by recent advances in 4D scene geometry reconstruction (e.g., $π^3$), the proposed method leverages reliable camera poses and rich spatial-temporal priors, which ensure stable training and robust inference for the model. Extensive experiments demonstrate that by eliminating complex pre-processing and iterative refinement, our approach achieves state-of-the-art motion segmentation performance with high efficiency. The code is available at:https://github.com/zjutcvg/GeoMotion.