SeMoLi: What Moves Together Belongs Together
CoRR(2024)
摘要
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 pseudo-label instances of moving objects and use
these as supervisory signals to train 3D object detectors in Lidar data without
manual supervision. We re-think this approach and suggest that both, object
detection, as well as motion-inspired pseudo-labeling, can be tackled in a
data-driven manner. We leverage recent advances in scene flow estimation to
obtain point trajectories from which we extract long-term, class-agnostic
motion patterns. Revisiting correlation clustering in the context of message
passing networks, we learn to group those motion patterns to cluster points to
object instances. By estimating the full extent of the objects, we obtain
per-scan 3D bounding boxes that we use to supervise a Lidar object detection
network. Our method not only outperforms prior heuristic-based approaches (57.5
AP, +14 improvement over prior work), more importantly, we show we can
pseudo-label and train object detectors across datasets.
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