  3D dynamic point clouds provide a discrete representation of real-world
objects or scenes in motion, which have been widely applied in immersive
telepresence, autonomous driving, surveillance, etc. However, point clouds
acquired from sensors are usually perturbed by noise, which affects downstream
tasks such as surface reconstruction and analysis. Although many efforts have
been made for static point cloud denoising, dynamic point cloud denoising
remains under-explored. In this paper, we propose a novel gradient-field-based
dynamic point cloud denoising method, exploiting the temporal correspondence
via the estimation of gradient fields -- a fundamental problem in dynamic point
cloud processing and analysis. The gradient field is the gradient of the
log-probability function of the noisy point cloud, based on which we perform
gradient ascent so as to converge each point to the underlying clean surface.
We estimate the gradient of each surface patch and exploit the temporal
correspondence, where the temporally corresponding patches are searched
leveraging on rigid motion in classical mechanics. In particular, we treat each
patch as a rigid object, which moves in the gradient field of an adjacent frame
via force until reaching a balanced state, i.e., when the sum of gradients over
the patch reaches 0. Since the gradient would be smaller when the point is
closer to the underlying surface, the balanced patch would fit the underlying
surface well, thus leading to the temporal correspondence. Finally, the
position of each point in the patch is updated along the direction of the
gradient averaged from corresponding patches in adjacent frames. Experimental
results demonstrate that the proposed model outperforms state-of-the-art
methods under both synthetic noise and simulated real-world noise.
