Abstract
For safety-critical robotics applications such as autonomous driving, it is important to detect all required objects accurately in real-time. Motion segmentation offers a solution by identifying dynamic objects from the scene in a class-agnostic manner. Recently, various motion segmentation models have been proposed, most of which jointly use subnetworks to estimate Depth, Pose, Optical Flow, and Scene Flow. As a result, the overall computational cost of the model increases, hindering real-time performance. In this paper, we propose a novel cost-volume-based motion feature representation, Channel-wise Motion Features. By extracting depth features of each instance in the feature map and capturing the scene's 3D motion information, it offers enhanced efficiency. The only subnetwork used to build Channel-wise Motion Features is the Pose Network, and no others are required. Our method not only achieves about 4 times the FPS of state-of-the-art models in the KITTI Dataset and Cityscapes of the VCAS-Motion Dataset, but also demonstrates equivalent accuracy while reducing the parameters to about 25$\%$.
Abstract (translated)
对于像自主驾驶这样的安全关键型机器人应用，准确地实时检测所有必需的对象非常重要。运动分割通过以类别无关的方式从场景中识别动态对象提供了一种解决方案。近年来，提出了各种运动分割模型，大多数这些模型共同使用子网络来估计深度、姿态、光流和场景流。因此，整个模型的计算成本增加，阻碍了其实时性能。在本文中，我们提出了一种新颖的成本体积基础运动特征表示方法——通道级运动特征(Channel-wise Motion Features)。通过提取特征图中每个实例的深度特性并捕捉场景的3D运动信息，这种方法提供了更高的效率。构建通道级运动特征所使用的唯一子网络是姿态网络(Pose Network)，不需要其他任何子网络。我们的方法不仅在KITTI数据集和VCAS-Motion数据集的城市景观部分实现了最先进的模型约4倍的每秒帧数(FPS)，而且还展示了等效的准确性，同时将参数减少到约25%。
URL
https://arxiv.org/abs/2507.13082
