Discrete-Continuous Depth Estimation from a Single Image
Abstract
In this paper, we tackle the problem of estimating the depth of a scene from a single image. This is a challenging task, since a single image on its own does not provide any depth cue. To address this, we exploit the availability of a pool of images for which the depth is known. More specifically, we formulate monocular depth estimation as a discrete-continuous optimization problem, where the continuous variables encode the depth of the superpixels in the input image, and the discrete ones represent relationships between neighboring superpixels. The solution to this discrete-continuous optimization problem is then obtained by performing inference in a graphical model using particle belief propagation. The unary potentials in this graphical model are computed by making use of the images with known depth. We demonstrate the effectiveness of our model in both the indoor and outdoor scenarios. Our experimental evaluation shows that our depth estimates are more accurate than existing methods on standard datasets.
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ImageNet pretraining improves accuracy for the compact model compared to training from scratch.
“Similar to [30,16], we start with weights pretrained on Ima-geNet [54], and show that this improves accuracy for our compact model compared to training from scratch (Table 2).”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [16]
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- Authors
- Liu, M., Salzmann, M., & He, X.
- Affiliations
- Australian National University (AU)
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 716-723
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2083047701
- Cited by
- 381
- References
- 28
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 14 · 2023: 21 · 2022: 37 · 2021: 56 · 2020: 48 · 2019: 69 · 2018: 60 · 2017: 26 · 2016: 24 · 2015: 10 · 2014: 2
- FWCI
- 11.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Media Technology › Image Processing Techniques and Applications
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- monocular depth estimation, superpixels, discrete-continuous optimization, probabilistic graphical models, unary potentials, indoor and outdoor scenes
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