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DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map

Peng Wang, Ruigang Yang, Binbin Cao, Wei Xu, Yuanqing Lin, CVPR, 2018
Baidu Research

Summary

  1. Contributions:
  • roposed a deep learning based system for fusing multiple sensors (i.e. RGB images, customer-grad GPS/IMU );
  • Jointly handle camera poses and scene semantics;
  • Create a dataset which includes dense 3D semantically labelled point clouds, GT camera poses and pixel-level semantic labels of video camera images.
  1. Dataset:
  • Data collection:

Mobile LIDAR scanner to collect point clouds and eliminate inaccurate moving objects; Two frontal cameras with a resolution of 2048×2432 to capture video.

  • 2D and 3D labeling
  1. Proposed Approach:
  • Render a label map from a camera pose:
  • Camera localization with motion prior:
    • Translation rectification with road prior, then a label map is rendered based on the rectified camera pose;
    • CNN-GRU pose network architecture:
      • CNN: inputs an image and the rendered label map (from corresponding coarse camera pose), and outputs a 7 dimension relative pose between the image and the rendered label map;
      • A multi-layer GRU with residual connection is appended to model the temporal dependency.
    • Pose loss: use the geometric matching loss for training, which avoids the balancing factor between rotation and translation.
  • Video parsing with pose guidance

Strengths / Novelties:

  • Using the pose after RNN, better alignment is achieved, and the segment accuracy is improved significantly;
  • The two information are mutually beneficial, where pose helps give good priori for segmentation and semantic guides a better localization;
  • Created a dataset which contains a point-cloud based semantic 3D map and videos with ground truth camera pose and per-frame semantic labelling.

Weaknesses / Notes:

  • GPS and IMU signal is very limited for training.