Scene flow characterizes the 3D motion between two LiDAR scans captured by an
autonomous vehicle at nearby timesteps. Prevalent methods consider scene flow as point …

Learning 3D scene flow from LiDAR point clouds presents significant difficulties including
poor generalization from synthetic datasets to real scenes scarcity of real-world 3D labels …

Scene flow estimation predicts the 3D motion at each point in successive LiDAR scans. This
detailed, point-level, information can help autonomous vehicles to accurately predict and …

We tackle semi-supervised object detection based on motion cues. Recent results suggest
that heuristic-based clustering methods in conjunction with object trackers can be used to …

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 …

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 …

The perception of 3D motion of surrounding traffic participants is crucial for driving safety.
While existing works primarily focus on general large motions we contend that the …

Scene flow estimation determines a scene's 3D motion field, by predicting the motion of
points in the scene, especially for aiding tasks in autonomous driving. Many networks with …

Global point clouds that correctly represent the static environment features can facilitate
accurate localization and robust path planning. However, dynamic objects introduce …

Reconstructing dynamic 3D scenes from 2D images and generating diverse views over time
presents a significant challenge due to the inherent complexity and temporal dynamics …