GitHub repository

Processes LiDAR data faster than the sensor frame rate, as shown in the demo video, enabling real-time applications like odometry and collision avoidance.

Works with any range-image-based LiDAR segmentation network such as SalsaNext or RangeNet++ by only changing the data loader and input, without modifying core pipelines.

Uses residual images from consecutive scans to capture motion cues, improving segmentation accuracy, as illustrated in the pipeline diagram.

Introduces a new benchmark based on SemanticKITTI for LiDAR-based moving object segmentation, providing a standard for evaluation and comparison.