Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds

    Abstract

    We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. In particular, our approach takes as input an image crop and sequentially produces vertices of the polygon outlining the object. This allows a human annotator to interfere at any time and correct a vertex if needed, producing as accurate segmentation as desired by the annotator. We show that our approach speeds up the annotation process by a factor of 4.7 across all classes in Cityscapes, while achieving 78.4% agreement in IoU with original ground-truth, matching the typical agreement between human annotators. For cars, our speed-up factor is 7.3 for an agreement of 82.2%. We further show generalization capabilities of our approach to unseen datasets.

    Authors

    Chris Zhang, Wenjie Luo, Raquel Urtasun

    Conference

    3DV 2018

    Full Paper

    ‘Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds’ (PDF)

    Uber ATG

    Comments
    Previous articleDeep Continuous Fusion for Multi-Sensor 3D Object Detection
    Next articleIncremental Few-Shot Learning with Attention Attractor Networks
    Wenjie Luo
    Wenjie is a senior research scientist, founding member of the Uber ATG R&D team. His research interests include computer vision and machine learning, and his work spans the full autonomy stack including perception, prediction and planning. Previously, he did master in TTI-Chicago and continued to the PhD program in University of Toronto, both under Prof. Raquel Urtasun. He also spent some time at Apple SPG prior to join Uber.
    Raquel Urtasun
    Raquel Urtasun is the Chief Scientist for Uber ATG and the Head of Uber ATG Toronto. She is also a Professor at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI. She is a recipient of an NSERC EWR Steacie Award, an NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, a Connaught New Researcher Award, a Fallona Family Research Award and two Best Paper Runner up Prize awarded CVPR in 2013 and 2017. She was also named Chatelaine 2018 Woman of the year, and 2018 Toronto’s top influencers by Adweek magazine