核心概念
SeMoLi is a data-driven method for pseudo-labeling moving objects in Lidar data, outperforming heuristic-based approaches and demonstrating cross-dataset generalization.
摘要
SeMoLi introduces a data-driven approach for pseudo-labeling moving objects in Lidar data.
The method leverages recent advances in scene flow estimation to extract long-term motion patterns.
SeMoLi utilizes correlation clustering in the context of message passing networks to segment point clouds and generate pseudo-labels for object detection.
The approach outperforms prior heuristic-based methods, showing improved performance with increasing data and generalization across datasets.
SeMoLi democratizes research in motion-inspired auto-labeling by making models, code, and baselines publicly available.
統計資料
SeMoLi는 휴리스틱 기반 방법을 능가하며 데이터 기반 방법을 소개합니다.
引述
"We leverage recent advances in scene flow estimation to obtain point trajectories."
"SeMoLi outperforms prior heuristic-based approaches and shows improved performance with increasing data."