---
license: cc-by-nc-sa-4.0
task_categories:
- depth-estimation
tags:
- monocular-depth
- kitti
- nyu-depth-v2
- depth-anything
---

# Monocular_Depth_Essentials

`Monocular_Depth_Essentials` is a lightweight, curated core dataset tailored specifically for **Monocular Depth Estimation** tasks. 

Following the data preparation guidelines from the classic [bts](https://github.com/cleinc/bts/tree/master/pytorch#nyu-depvh-v2) repository , this dataset extracts only the essential image-depth pairs from the massive raw **KITTI** and **NYU Depth V2** datasets based strictly on the official **Eigen Split** `train/test` text lists.

If you are benchmarking or reproducing **Depth Anything (V1/V2)**, **BTS**, or other monocular depth estimation models, this dataset allows you to bypass the tedious raw data downloading, filtering, and cleaning phases, offering a true "plug-and-play" experience.

---

## 📌 Dataset Features & Structure

* **Streamlined Data**: Redundant sequences not evaluated in standard benchmarks are excluded, leaving only the exact samples specified by the official `txt` splits.
* **Standard Evaluation**: Fully adheres to the academic standard **Eigen Split**, ensuring fair and direct experimental comparison.
* **Clean Directory**: Organised to seamlessly align with dataloaders in mainstream depth estimation codebases.