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iDisc: Internal Discretization for Monocular Depth Estimation [CVPR 2023]
| Date | Stars |
|---|---|
| 2026-07-24 | 292 |
| 2026-07-25 | 292 |
| 2026-07-28 | 292 |
| 2026-07-30 | 292 |
| 2026-08-06 | 292 |
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[](https://paperswithcode.com/sota/monocular-depth-estimation-on-kitti-eigen?p=idisc-internal-discretization-for-monocular)
[](https://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction)
[](https://paperswithcode.com/sota/monocular-depth-estimation-on-nyu-depth-v2?p=idisc-internal-discretization-for-monocular)
[](https://paperswithcode.com/sota/surface-normals-estimation-on-nyu-depth-v2-1?p=idisc-internal-discretization-for-monocular)
# iDisc: Internal Discretization for Monocular Depth Estimation
> [**iDisc: Internal Discretization for Monocular Depth Estimation**](),
> Luigi Piccinelli, Christos Sakaridis, Fisher Yu,
> CVPR 2023 (to appear)
> *Project Website ([iDisc](http://vis.xyz/pub/idisc/))*
> *Paper ([arXiv 2304.06334](https://arxiv.org/pdf/2304.06334.pdf))*
## Visualization
### KITTI
<p align="center">
<img src="docs/kitti_example.gif" alt="animated" />
</p>
### NYUv2-Depth
<p align="center">
<img src="docs/nyu_example.gif" alt="animated" />
</p>
For more, and not compressed, visual examples please visit [vis.xyz](http://vis.xyz/pub/idisc/).
## Citation
If you find our work useful in your research please consider citing our publication:
```bibtex
@inproceedings{piccinelli2023idisc,
title={iDisc: Internal Discretization for Monocular Depth Estimation},
author={Piccinelli, Luigi and Sakaridis, Christos and Yu, Fisher},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}
```
## Abstract
Monocular depth estimation is fundamental for 3D scene understanding and downstream applications. However, even under the supervised setup, it is still challenging and ill posed due to the lack of geometric constraints. We observe that although a scene can consist of millions of pixels, there are much fewer high-level patterns. We propose iDisc to learn those patterns with internal discretized representations. The method implicitly partitions the scene into a set of high-level concepts. In particular, our new module, Internal Discretization (ID), implements a continuous-discrete-continuous bottleneck to learn those concepts without supervision. In contrast to state-of-the-art methods, the proposed model does not enforce any explicit constraints or priors on the depth output. The whole network with the ID module can be trained in an end-to-end fashion thanks to the bottleneck module based on attention. Our method sets the new state of the art with significant improvements on NYU-Depth v2 and KITTI, outperforming all published methods on the official KITTI benchmark. iDisc can also achieve state-of-the-art results on surface normal estimation. Further, we explore the model generalization capability via zero-shot testing. From there, we observe the compelling need to promote diversification in the outdoor scenario and we introduce splits of two autonomous driving datasets, DDAD and Argoverse
## Installation
Please refer to [INSTALL.md](docs/INSTALL.md) for installation and to [DATA.md](docs/DATA.md) for datasets preparation.
## Get Started
Please see [GETTING_STARTED.md](docs/GETTING_STARTED.md) for the basic usage of iDisc.
## Model Zoo
### General
We store the output predictions in the same relative path as the depth path from the corresponding dataset. For evaluation we usExcerpt of 10,184 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6956d802623b265a, topic:computer-vision, readme:computer vision, desc:depth estimation
matched fp:6956d802623b265a, topic:deep-learning