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公共数据集

NAS:/Public/datasets 文件夹用于存储公共数据集（网上发布的公开数据集），只读。将数据集提交到 NAS:/datasets 可以节省 home 文件夹的配额。

所有用户都可以通过以下方式提交自己下载的数据集：

  1. 下载数据集到个人 home 文件夹
  2. 将数据集解压到个人 home 文件夹任意位置，以数据集名称命名，命名应不包含空格，空格以 '_' 替代
  3. 将 nas_directory 仓库 fork 到自己的 github 并克隆到个人电脑，编辑 datasets/README.md，按格式添加数据集的信息
  4. 完善 commit 信息（添加公共数据集 {dataset_name}）并 commit，然后 Push 到 fork 的 github 仓库
  5. 向原仓库的 master 分支发起 pull request，填写 issue：
    • 标题（title）：添加公共数据集 {dataset_name}
    • 描述（description）：数据集所在路径（如 homes/{user_name}/datasets/{dataset_name}）
    • 标签（labels）选择：dataset
  6. 交由 managers（管理员请参考公共数据集入库说明）进行 merge 并将数据集入库到 NAS:/Public/datasets
  7. managers 工作结束后，用户检查提交的数据集是否正确拷贝到 NAS:/Public/datasets，README 是否正确更新，如果一切顺利，便可删除自己家目录的对应数据集

模板：CIFAR-10

HDR+

Scene Flow

MIT Adobe5K

  • 主页：The FiveK dataset
  • 关键词：图像增强，HDR，image enhancement
  • 引用：Bychkovsky, Vladimir, et al. "Learning photographic global tonal adjustment with a database of input/output image pairs." CVPR 2011. IEEE, 2011.
  • 相关链接：
  • 说明：We collected 5,000 photographs taken with SLR cameras by a set of different photographers. They are all in RAW format; that is, all the information recorded by the camera sensor is preserved. We made sure that these photographs cover a broad range of scenes, subjects, and lighting conditions. We then hired five photography students in an art school to adjust the tone of the photos. Each of them retouched all the 5,000 photos using a software dedicated to photo adjustment (Adobe Lightroom) on which they were extensively trained. We asked the retouchers to achieve visually pleasing renditions, akin to a postcard. The retouchers were compensated for their work.
  • 目录：adobe5k
    • raw_photos/HQ1to5000: 5000 .dng RAW image files.
    • fivek_c: 5000 .tif RGB images well-touched by Expert C.
  • 上传者：陈才

ShapeNet

  • 主页：ShapeNet
  • 关键词：3D semantic/instance segmentation，3D representation, 3D scene understanding, 3D scene parsing
  • 引用：ShapeNet: An Information-Rich 3D Model Repository
  • 相关链接：
  • 说明：
    • What is ShapeNet?
      • ShapeNet is an ongoing effort to establish a richly-annotated, large-scale dataset of 3D shapes.
      • We provide researchers around the world with this data to enable research in computer graphics, computer vision, robotics, and other related disciplines. ShapeNet is a collaborative effort between researchers at Princeton, Stanford and TTIC. ShapeNet is organized according to the WordNet hierarchy.
      • Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, the majority of them being nouns (80,000+)
    • ShapeNet is made of several different subsets:
      • ShapeNetCore
        • ShapeNetCore is a subset of the full ShapeNet dataset with single clean 3D models and manually verified category
        • and alignment annotations. It covers 55 common object categories with about 51,300 unique 3D models. The 12 object categories of PASCAL 3D+, a popular computer vision 3D benchmark dataset, are all covered by ShapeNetCore.
      • ShapeNetSem
        • ShapeNetSem is a smaller, more densely annotated subset consisting of 12,000 models spread over a broader set of
        • 270 categories. In addition to manually verified category labels and consistent alignments, these models are annotated with real-world dimensions, estimates of their material composition at the category level, and estimates of their total volume and weight.
  • 目录：ShapeNet
  • 上传者：章程

SID

  • 主页：Learning-to-see-in-the-dark dataset
  • 关键词：低光照图像去噪，Raw image processing
  • 引用：Chen, Chen et al. “Learning to See in the Dark.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)
  • 相关链接：
    • Github: 有关如何引用数据集的信息，请参阅详细说明
  • 说明：Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can lead to blurry images and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To support the development of learning-based pipelines for low-light image processing, we introduce a dataset of raw short-exposure night-time images, with corresponding long-exposure reference images. Using the presented dataset, we develop a pipeline for processing low-light images, based on end-to-end training of a fully-convolutional network. The network operates directly on raw sensor data and replaces much of the traditional image processing pipeline, which tends to perform poorly on such data. We report promising results on the new dataset, analyze factors that affect performance, and highlight opportunities for future work
  • 目录：SID/Sony (The Fuji subset is not downloaded currently)
  • 上传者：陈才

Zrr

  • 主页：Zurich RAW to RGB mapping dataset
  • 关键词：Raw图像处理，图像增强，ISP pipeline
  • 引用：Replacing Mobile Camera ISP with a Single Deep Learning Model
  • 相关链接：
    • Github: 有关如何引用数据集的信息，请参阅详细说明
  • 说明：The dataset consists of 3640 bursts (made up of 28461 images in total), organized into subfolders, plus the results of our image processing pipeline. Each burst consists of the raw burst input (in DNG format) and certain metadata not present in the images, as sidecar files. For results, we provide both the intermediate result of aligning and merging the frames (also in DNG format), and the final result of our pipeline (as a JPG).
  • 目录：zrr
  • 上传者：陈才

KITTI

  • 主页：KITTI dataset
  • 关键词：自动驾驶，深度估计，立体匹配，光流估计，3D检测分割
  • 说明：目前下载了 depth、stereo、optical flow、scene flow 几种数据，还有各种任务的 tools。有关如何引用数据集的信息，请参阅详细主页
  • 目录：kitti/rvc_devkit/
    • depth
    • stereo
  • 上传者：陈才

SUNCG

Pix3D

  • 主页：Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
  • 关键词：单目 3D 重建，3D shape modeling from a single image
  • 引用：Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
  • 说明：We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications in shape-related tasks including reconstruction, retrieval, viewpoint estimation, etc. Building such a large-scale dataset, however, is highly challenging; existing datasets either contain only synthetic data, or lack precise alignment between 2D images and 3D shapes, or only have a small number of images. Second, we calibrate the evaluation criteria for 3D shape reconstruction through behavioral studies, and use them to objectively and systematically benchmark cuttingedge reconstruction algorithms on Pix3D. Third, we design a novel model that simultaneously performs 3D reconstruction and pose estimation; our multi-task learning approach achieves state-of-the-art performance on both tasks.
  • 目录：Pix3D
  • 上传者：章程

SUN RGB-D

  • 主页：SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite
  • 关键词：3D 场景理解，scene understanding，scene classification，语义分割，semantic segmantation，房间布局，room layout，3D 检测，detection，姿态估计，pose estimation
  • 引用：SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite
  • 说明：Although RGB-D sensors have enabled major breakthroughs for several vision tasks, such as 3D reconstruction, we haven not achieved a similar performance jump for high-level scene understanding. Perhaps one of the main reasons for this is the lack of a benchmark of reasonable size with 3D annotations for training and 3D metrics for evaluation. In this paper, we present an RGB-D benchmark suite for the goal of advancing the state-of-the-art in all major scene understanding tasks. Our dataset is captured by four different sensors and contains 10,000 RGB-D images, at a similar scale as PASCAL VOC. The whole dataset is densely annotated and includes 146,617 2D polygons and 58,657 3D bounding boxes with accurate object orientations, as well as a 3D room layout and category for scenes. This dataset enables us to train data-hungry algorithms for scene-understanding tasks, evaluate them using direct and meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias.
  • 目录：SUNRGBD
  • 上传者：章程

3D-FRONT

  • 主页：3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics
  • 关键词：3D 场景理解，scene understanding，scene classification，语义分割，semantic segmantation，房间布局，room layout，3D 检测，detection，姿态估计，pose estimation，合成场景
  • 引用：3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics, 3D-FUTURE: 3D Furniture shape with TextURE
  • 说明：A new, large-scale, and comprehensive repository of synthetic indoor scenes highlighted by professionally designed layouts and a large number of rooms populated by high-quality textured 3D models with style compatibility. From layout semantics down to texture details of individual objects, our dataset is freely available to the academic community and beyond. Currently, 3D-FRONT contains 18,797 rooms diversely furnished by 3D objects, far surpassing all publicly available scene datasets. In addition, the 7,302 furniture objects all come with high-quality textures. While the floorplans and layout designs are directly sourced from professional creations, the interior designs in terms of furniture styles, color, and textures have been carefully curated based on a recommender system we develop to attain consistent styles as expert designs. Furthermore, we release Trescope, a light-weight rendering tool, to support benchmark rendering of 2D images and annotations from 3D-FRONT. We demonstrate two applications, interior scene synthesis and texture synthesis, that are especially tailored to the strengths of our new dataset.
  • 目录：3D-FRONT
    • 3d-front terms of use.pdf: 使用条款
    • Requesting for download links of 3D-FRONT data.pdf: 申请回复
    • 3D-FRONT: house layouts
    • 3D-FUTURE-model: furniture shapes
  • 上传者：章程