论文标题
SAMP：4D车辆重建的形状和运动先验
SAMP: Shape and Motion Priors for 4D Vehicle Reconstruction
论文作者
论文摘要
从可移动平台中推断出3D中车辆的姿势和形状仍然是一项具有挑战性的任务，因为相机的投射感应原理，艰难的表面特性，例如反射或透明度和图像之间的照明变化。在本文中，我们建议使用3D形状和运动先验来以立体声图像的序列进行轨迹和车辆形状的估计。我们以3D签名的距离函数表示形状，并将它们嵌入低维歧管中。我们的优化方法允许在对象轨道上施加所有图像观测值的共同形状。我们采用运动模型将轨迹正规化为合理的对象运动。我们在Kitti数据集上评估了我们的方法，并以形状重建和姿势估计精度显示了最新结果。
Inferring the pose and shape of vehicles in 3D from a movable platform still remains a challenging task due to the projective sensing principle of cameras, difficult surface properties e.g. reflections or transparency, and illumination changes between images. In this paper, we propose to use 3D shape and motion priors to regularize the estimation of the trajectory and the shape of vehicles in sequences of stereo images. We represent shapes by 3D signed distance functions and embed them in a low-dimensional manifold. Our optimization method allows for imposing a common shape across all image observations along an object track. We employ a motion model to regularize the trajectory to plausible object motions. We evaluate our method on the KITTI dataset and show state-of-the-art results in terms of shape reconstruction and pose estimation accuracy.
