Abstract 3D scene flow estimation aims to estimate point-wise motions between two
consecutive frames of point clouds. Superpoints, ie, points with similar geometric features …

Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow
estimation has been an interest of research in computer vision and computer graphics for a …

Scene flow estimation, which predicts the 3D displacements of point clouds, is a
fundamental task in autonomous driving. Most methods have adopted a coarse-to-fine …

State-of-the-art scene flow methods broadly fail to describe the motion of small objects, and
existing evaluation protocols hide this failure by averaging over many points. To address this …

As a low-level task of 3D perception, scene flow is a fundamental representation of dynamic
scenes and provides non-rigid motion descriptions for the objects in the 3D environment …

The test-time optimization of scene flow—using a coordinate network as a neural prior [27]—
has gained popularity due to its simplicity, lack of dataset bias, and state-of-the-art …

Since estimating scene flow from point clouds is challenging, some methods involve the
robust Transformer. However, there are two problems with these methods:(1) Dense …

Scene flow estimation, which predicts the 3D motion of scene points from point clouds, is a
core task in autonomous driving and many other 3D vision applications. Existing methods …

Scene flow describes the 3D motion in a scene. It can be modeled as a single task or as a
composite of the auxiliary tasks of depth, camera motion, and optical flow estimation. Deep …

Large models have shown generalization across datasets for many low-level vision tasks,
like depth estimation, but no such general models exist for scene flow. Even though scene …