用隐式4D几何直接端到端分割运动物体，效率更高。
GeoMotion: Rethinking Motion Segmentation via Latent 4D Geometry
- 通过注意力机制从隐特征中直接分割运动物体，跳过传统匹配步骤。
- 在MVD、Kitti360等数据集上达到最新精度，推理速度更快。
- 适合需要高效高精度运动分割的自动驾驶与视频理解场景。
动态场景中的运动分割极具挑战性，传统方法依赖噪声较大的运动线索估计相机位姿和点对应关系，多阶段流程中的累积误差导致性能受限或计算开销大。本文提出一种完全基于学习的方法，通过注意力机制从隐式特征表示中直接推断运动物体，实现端到端前馈分割。核心思想是绕过显式对应估计，让模型隐式分离物体与相机运动。借助近期4D场景几何重建（如π³）的进展，该方法利用可靠的相机位姿和丰富的时空先验，确保训练稳定与推理鲁棒。大量实验表明，通过省去复杂预处理和迭代优化，本方法在保持高效率的同时实现了当前最优的运动分割性能。代码已开源：https://github.com/zjutcvg/GeoMotion。
原文摘要 · Abstract (English)
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.
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