Self-supervised Scene Flow Estimation
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Benchmarks
Argoverse 2
Most implemented
SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
3D Object Detection with a Self-supervised Lidar Scene Flow Backbone
Self-Supervised Scene Flow Estimation with 4-D Automotive Radar
PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds
VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow
ICP-Flow: LiDAR Scene Flow Estimation with ICP
Papers
Motion Cues from Image-based Point Tracking for LiDAR Scene Flow Estimation
LiDAR scene flow estimation is essential for autonomous driving, as it provides 3D motion for each point. Self-supervised approaches use static-dynamic classification to mitigate the imbalance between static and dynamic …
Self-supervised Scene Flow EstimationAutonomous DrivingPoint TrackingVoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow
Scene flow estimation aims to recover per-point motion from two adjacent LiDAR scans. However, in real-world applications such as autonomous driving, points rarely move independently of others, especially for nearby poin…
Autonomous DrivingComputational EfficiencyInductive BiasScene Flow Estimation+1Self-Supervised Scene Flow Estimation with Point-Voxel Fusion and Surface Representation
Scene flow estimation aims to generate the 3D motion field of points between two consecutive frames of point clouds, which has wide applications in various fields. Existing point-based methods ignore the irregularity of …
Scene Flow EstimationSelf-supervised Scene Flow EstimationSeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
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 understand dynamic changes in their sur…
Autonomous DrivingAutonomous VehiclesObjectScene Flow Estimation+1ICP-Flow: LiDAR Scene Flow Estimation with ICP
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-wise unconstrained flow vectors that can be learned by …
Autonomous DrivingScene Flow EstimationSelf-supervised Scene Flow EstimationFedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
Roadside unit (RSU) can significantly improve the safety and robustness of autonomous vehicles through Vehicle-to-Everything (V2X) communication. Currently, the usage of a single RSU mainly focuses on real-time inference…
Autonomous VehiclesFederated LearningScene Flow EstimationSelf-Supervised Learning+1