Corpus ID: 232478660

Full Surround Monodepth from Multiple Cameras

@article{Guizilini2021FullSM,
  title={Full Surround Monodepth from Multiple Cameras},
  author={V. Guizilini and Igor Vasiljevic and Rares Ambrus and G. Shakhnarovich and Adrien Gaidon},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.00152}
}
Self-supervised monocular depth and ego-motion estimation is a promising approach to replace or supplement expensive depth sensors such as LiDAR for robotics applications like autonomous driving. However, most research in this area focuses on a single monocular camera or stereo pairs that cover only a fraction of the scene around the vehicle. In this work, we extend monocular self-supervised depth and ego-motion estimation to large-baseline multicamera rigs. Using generalized spatio-temporal… Expand

References

SHOWING 1-10 OF 53 REFERENCES
Digging Into Self-Supervised Monocular Depth Estimation
TLDR
It is shown that a surprisingly simple model, and associated design choices, lead to superior predictions, and together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods. Expand
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
TLDR
This paper proposes a geometry consistency loss for scale-consistent predictions and an induced self-discovered mask for handling moving objects and occlusions and is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale- Consistent camera trajectories over a long video sequence. Expand
Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion
TLDR
It is shown that self-supervision can be used to learn accurate depth and ego-motion estimation without prior knowledge of the camera model, and Neural Ray Surfaces (NRS) are introduced, convolutional networks that represent pixel-wise projection rays, approximating a wide range of cameras. Expand
Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance
TLDR
A new self-supervised semantically-guided depth estimation (SGDepth) method to deal with moving dynamic-class (DC) objects, such as moving cars and pedestrians, which violate the static-world assumptions typically made during training of such models. Expand
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
TLDR
The main contribution is to explicitly consider the inferred 3D geometry of the whole scene, and enforce consistency of the estimated 3D point clouds and ego-motion across consecutive frames, and outperforms the state-of-the-art for both breadth and depth. Expand
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss
TLDR
Experimental results on the KITTI dataset show that the proposed algorithm outperforms the state-of-the-art unsupervised methods using monocular sequences, and achieves comparable or even better result compared to unsuper supervised methods using stereo sequences. Expand
Semi-Supervised Deep Learning for Monocular Depth Map Prediction
TLDR
This paper proposes a novel approach to depth map prediction from monocular images that learns in a semi-supervised way and uses sparse ground-truth depth for supervised learning, and also enforces the deep network to produce photoconsistent dense depth maps in a stereo setup using a direct image alignment loss. Expand
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume
TLDR
This work presents a novel architecture specifically designed for spherical disparity using the setting of top-bottom 360° camera pairs, and proposes to mitigate the distortion issue by an additional input branch capturing the position and relation of each pixel in the spherical coordinate. Expand
Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras
TLDR
This work is the first to learn the camera intrinsic parameters, including lens distortion, from video in an unsupervised manner, thereby allowing us to extract accurate depth and motion from arbitrary videos of unknown origin at scale. Expand
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation
TLDR
It is shown that high resolution is key towards high-fidelity self-supervised monocular depth prediction, and a subpixel convolutional layer extension for depth super-resolution is proposed that accurately synthesizes high-resolution disparities from their corresponding low-resolution Convolutional features. Expand
...
1
2
3
4
5
...