In this paper, we propose a novel self-supervised motion estimator for
LiDAR-based autonomous driving via BEV representation. Different from usually
adopted self-supervised strategies for data-level structure consistency, we
predict scene motion via feature-level consistency between pillars in
consecutive frames, which can eliminate the effect caused by noise points and
view-changing point clouds in dynamic scenes. Specifically, we propose
\textit{Soft Discriminative Loss} that provides the network with more
pseudo-supervised signals to learn discriminative and robust features in a
contrastive learning manner. We also propose \textit{Gated Multi-frame Fusion}
block that learns valid compensation between point cloud frames automatically
to enhance feature extraction. Finally, \textit{pillar association} is proposed
to predict pillar correspondence probabilities based on feature distance, and
whereby further predicts scene motion. Extensive experiments show the
effectiveness and superiority of our \textbf{ContrastMotion} on both scene flow
and motion prediction tasks. The code is available soon.

提出一种基于 LiDAR 的自主驾驶的自监督运动估计器，采用特征级一致性预测场景运动，使用 Soft 判别损失进行对比学习，使用门控多帧融合块自动学习点云帧之间的有效补偿，最终通过支柱关联预测支柱对应概率，进而预测场景运动。

ContrastMotion: 大规模 LiDAR 点云自监督场景运动学习

ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale  LiDAR Point Clouds

The key success factor of the video deblurring methods is to compensate for
the blurry pixels of the mid-frame with the sharp pixels of the adjacent video
frames. Therefore, mainstream methods align the adjacent frames based on the
estimated optical flows and fuse the alignment frames for restoration. However,
these methods sometimes generate unsatisfactory results because they rarely
consider the blur levels of pixels, which may introduce blurry pixels from
video frames. Actually, not all the pixels in the video frames are sharp and
beneficial for deblurring. To address this problem, we propose the
spatio-temporal deformable attention network (STDANet) for video delurring,
which extracts the information of sharp pixels by considering the pixel-wise
blur levels of the video frames. Specifically, STDANet is an encoder-decoder
network combined with the motion estimator and spatio-temporal deformable
attention (STDA) module, where motion estimator predicts coarse optical flows
that are used as base offsets to find the corresponding sharp pixels in STDA
module. Experimental results indicate that the proposed STDANet performs
favorably against state-of-the-art methods on the GoPro, DVD, and BSD datasets.

提出了一种运用 Spatio-Temporal Deformable Attention Network (STDANet) 实现视频去模糊的方法，STDANet 不仅仅考虑图像中的流动信息，同时考虑图像中像素点的清晰度，从而使得方法得到的清晰显著提高。