Seamless Scene Segmentation

CVPR 2019 Lorenzo PorziSamuel Rota BulòAleksander ColovicPeter Kontschieder

In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results. Our goal is to predict consistent semantic segmentation and detection results by means of a panoptic output format, going beyond the simple combination of independently trained segmentation and detection models... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Panoptic Segmentation Indian Driving Dataset Seamless PQ 48.5 # 2
Panoptic Segmentation KITTI Panoptic Segmentation Seamless PQ 42.2 # 2

Methods used in the Paper


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