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The progress in maritime obstacle detection is hindered by the lack of adiverse dataset that adequately captures the complexity of general maritimeenvironments. We present the first maritime panoptic obstacle detectionbenchmark LaRS, featuring scenes from Lakes, Rivers and Seas. Our majorcontribution is the new dataset, which boasts the largest diversity inrecording locations, scene types, obstacle classes, and acquisition conditionsamong the related datasets. LaRS is composed of over 4000 per-pixel labeled keyframes with nine preceding frames to allow utilization of the temporal texture,amounting to over 40k frames. Each key frame is annotated with 8 thing, 3 stuffclasses and 19 global scene attributes. We report the results of 27 semanticand panoptic segmentation methods, along with several performance insights andfuture research directions. To enable objective evaluation, we have implementedan online evaluation server. The LaRS dataset, evaluation toolkit and benchmarkare publicly available at: https://lojzezust.github.io/lars-dataset

LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark