SeMoLi: Data-Driven Object Detection in Lidar with Motion Cues

Kernekoncepter

SeMoLi introduces a data-driven approach for object detection in Lidar using motion cues, outperforming heuristic-based methods and demonstrating cross-dataset generalization.

Resumé

SeMoLi proposes a novel method for segmenting moving objects in Lidar data using correlation clustering and message passing networks. By leveraging long-term motion patterns, SeMoLi generates pseudo-labels to train object detectors across datasets. The approach shows significant improvements over prior methods, especially when trained on increasing amounts of data. SeMoLi's performance remains robust even with noisy estimated motion information, showcasing its generalizability and effectiveness in real-world applications.

Statistik

Our method not only outperforms prior heuristic-based approaches (57.5 AP, +14 improvement over prior work)
SeMoLi consistently performs favorably compared to all DBSCAN variants when using perfect "oracle" motion cues.
SeMoLi benefits from the increased amount of training data, reaching 57.6@0.4 F1 with expanding training set.

Citater

"We propose SeMoLi, a data-driven approach for segmenting moving instances in point clouds."
"Our method not only outperforms prior heuristic-based approaches but also demonstrates cross-dataset generalization."

Vigtigste indsigter udtrukket fra

SeMoLi

by Jenn... kl. arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19463.pdf

Dybere Forespørgsler

How can the concept of motion-inspired pseudo-labeling be applied to other domains beyond object detection

The concept of motion-inspired pseudo-labeling can be applied to various domains beyond object detection. For example, in the field of autonomous navigation, this approach could be utilized for scene understanding and obstacle avoidance by identifying moving entities such as pedestrians or vehicles. In robotics, it could aid in tracking dynamic objects in complex environments for tasks like manipulation or interaction. Additionally, in surveillance systems, motion-inspired pseudo-labeling could enhance activity recognition and anomaly detection by focusing on movements within a scene.

What are the potential limitations or biases introduced by utilizing pseudo-labels generated by SeMoLi for training object detectors

When utilizing pseudo-labels generated by SeMoLi for training object detectors, there are potential limitations and biases to consider. One limitation is the reliance on accurate motion estimation from Lidar data; any errors or noise in the estimated trajectories can lead to mislabeled instances and impact the performance of the detector. Biases may arise if certain classes of moving objects are overrepresented or underrepresented in the pseudo-labeled data, affecting the model's ability to generalize across diverse scenarios. Moreover, since pseudo-labels are generated based on assumptions about spatial proximity and common fate principles, there may be instances where these assumptions do not hold true, leading to inaccuracies in labeling.

How might advancements in Lidar technology impact the performance and applicability of SeMoLi in future scenarios

Advancements in Lidar technology can significantly impact the performance and applicability of SeMoLi in future scenarios. Higher resolution Lidar sensors with increased range capabilities would provide more detailed point cloud data for better segmentation accuracy during motion clustering. Improved sensor fusion techniques integrating Lidar with other modalities like cameras or radar could enhance the robustness of SeMoLi against environmental variations and occlusions. Furthermore, advancements enabling real-time processing speeds would make SeMoLi more practical for applications requiring quick decision-making based on moving object detection results.