Self-supervised monocular depth estimation presents a powerful method to obtain 3D scene
information from single camera images, which is trainable on arbitrary image sequences …

We propose a novel end-to-end deep scene flow model, called PointPWC-Net, that directly
processes 3D point cloud scenes with large motions in a coarse-to-fine fashion. Flow …

Unsupervised learning of optical flow, which leverages the supervision from view synthesis,
has emerged as a promising alternative to supervised methods. However, the objective of …

This work proposes a novel approach to 4D radar-based scene flow estimation via cross-
modal learning. Our approach is motivated by the co-located sensing redundancy in modern …

Unsupervised monocular depth estimation techniques have demonstrated encouraging
results but typically assume that the scene is static. These techniques suffer when trained on …

In this work, we focus on scene flow learning on point clouds in a self-supervised manner. A
real-world scene can be well modeled as a collection of rigidly moving parts, therefore its …

We present an unsupervised learning approach for optical flow estimation by improving the
upsampling and learning of pyramid network. We design a self-guided upsample module to …

Scene flow estimation has been receiving increasing attention for 3D environment
perception. Monocular scene flow estimation-obtaining 3D structure and 3D motion from two …

State-of-the-art self-supervised learning approaches for monocular depth estimation usually
suffer from scale ambiguity. They do not generalize well when applied on distance …

Reliable multi-agent trajectory prediction is crucial for the safe planning and control of
autonomous systems. Compared with single-agent cases, the major challenge in …