--- license: apache-2.0 language: - en multilinguality: - monolingual size_categories: - 10K, 'depth_map': , } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `depth_map`: A `PIL.Image.Image` object containing the annotation depth map. ### Data Splits The data is split into training, and validation splits. The training data contains 47584 images, and the validation data contains 654 images. ## Visualization You can use the following code snippet to visualize samples from the dataset: ```py from datasets import load_dataset import numpy as np import matplotlib.pyplot as plt cmap = plt.cm.viridis ds = load_dataset("sayakpaul/nyu_depth_v2") def colored_depthmap(depth, d_min=None, d_max=None): if d_min is None: d_min = np.min(depth) if d_max is None: d_max = np.max(depth) depth_relative = (depth - d_min) / (d_max - d_min) return 255 * cmap(depth_relative)[:,:,:3] # H, W, C def merge_into_row(input, depth_target): input = np.array(input) depth_target = np.squeeze(np.array(depth_target)) d_min = np.min(depth_target) d_max = np.max(depth_target) depth_target_col = colored_depthmap(depth_target, d_min, d_max) img_merge = np.hstack([input, depth_target_col]) return img_merge random_indices = np.random.choice(len(ds["train"]), 9).tolist() train_set = ds["train"] plt.figure(figsize=(15, 6)) for i, idx in enumerate(random_indices): ax = plt.subplot(3, 3, i + 1) image_viz = merge_into_row( train_set[idx]["image"], train_set[idx]["depth_map"] ) plt.imshow(image_viz.astype("uint8")) plt.axis("off") ``` ## Dataset Creation ### Curation Rationale The rationale from [the paper](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) that introduced the NYU Depth V2 dataset: > We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships. One of our main interests is to better understand how 3D cues can best inform a structured 3D interpretation. ### Source Data #### Initial Data Collection > The dataset consists of 1449 RGBD images, gathered from a wide range of commercial and residential buildings in three different US cities, comprising 464 different indoor scenes across 26 scene classes.A dense per-pixel labeling was obtained for each image using Amazon Mechanical Turk. ### Annotations #### Annotation process This is an involved process. Interested readers are referred to Sections 2, 3, and 4 of the [original paper](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf). #### Who are the annotators? AMT annotators. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators * Original NYU Depth V2 dataset: Nathan Silberman, Derek Hoiem, Pushmeet Kohli, Rob Fergus * Preprocessed version: Diana Wofk, Fangchang Ma, Tien-Ju Yang, Sertac Karaman, Vivienne Sze ### Licensing Information The preprocessed NYU Depth V2 dataset is licensed under a [MIT License](https://github.com/dwofk/fast-depth/blob/master/LICENSE). ### Citation Information ```bibtex @inproceedings{Silberman:ECCV12, author = {Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus}, title = {Indoor Segmentation and Support Inference from RGBD Images}, booktitle = {ECCV}, year = {2012} } @inproceedings{icra_2019_fastdepth, author = {{Wofk, Diana and Ma, Fangchang and Yang, Tien-Ju and Karaman, Sertac and Sze, Vivienne}}, title = {{FastDepth: Fast Monocular Depth Estimation on Embedded Systems}}, booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}}, year = {{2019}} } ``` ### Contributions Thanks to [@sayakpaul](https://huggingface.co/sayakpaul) for adding this dataset.