Surface Normals Estimation

ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects. Up to now, ScanNet v2, the newest version of ScanNet, has collected 1513 annotated scans with an approximate 90% surface coverage. In the semantic segmentation task, this dataset is marked in 20 classes of annotated 3D voxelized objects. Source: A Review of Point Cloud Semantic Segmentation Image Source: http://www.scan-net.org/

The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. It features: * 1449 densely labeled pairs of aligned RGB and depth images * 464 new scenes taken from 3 cities * 407,024 new unlabeled frames * Each object is labeled with a class and an instance number. The dataset has several components: * Labeled: A subset of the video data accompanied by dense multi-class labels. This data has also been preprocessed to fill in missing depth labels. * Raw: The raw RGB, depth and accelerometer data as provided by the Kinect. * Toolbox: Useful functions for manipulating the data and labels. Source: https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html Image Source: https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html

The PASCAL Context dataset is an extension of the PASCAL VOC 2010 detection challenge, and it contains pixel-wise labels for all training images. It contains more than 400 classes (including the original 20 classes plus backgrounds from PASCAL VOC segmentation), divided into three categories (objects, stuff, and hybrids). Many of the object categories of this dataset are too sparse and; therefore, a subset of 59 frequent classes are usually selected for use. Source: Image Segmentation Using Deep Learning:A Survey Image Source: https://cs.stanford.edu/~roozbeh/pascal-context/

Taskonomy provides a large and high-quality dataset of varied indoor scenes. - Complete pixel-level geometric information via aligned meshes. - Semantic information via knowledge distillation from ImageNet, MS COCO, and MIT Places. - Globally consistent camera poses. Complete camera intrinsics. - High-definition images. - 3x times big as ImageNet. Source: Taskonomy Image Source: http://taskonomy.stanford.edu/

iBims-1 (independent Benchmark images and matched scans - version 1) is a new high-quality RGB-D dataset, especially designed for testing single-image depth estimation (SIDE) methods. A customized acquisition setup, composed of a digital single-lens reflex (DSLR) camera and a high-precision laser scanner was used to acquire high-resolution images and highly accurate depth maps of diverse indoors scenarios. Compared to related RGB-D datasets, iBims-1 stands out due to a very low noise level, sharp depth transitions, no occlusions, and high depth ranges. Our dataset consists of the following components: Core dataset: - 100 RGB-D image pairs of various indoor scenes in high- and low resolution - Masks for invalid, transparent and planar regions (tables, floors, walls) - Masks for distinct depth transitions - Camera calibration parameters Auxiliary dataset: - 56 different color and geometric augmentations for each image of the core dataset - Additional hand-held images for testing MVS methods - Images of printed patterns and photos posted on a wall to assess performance of textured planar surfaces - Several RGB-D image sequences of static scenes with varying illumation Source: Evaluation of CNN-based Single-Image Depth Estimation Methods Image source: https://arxiv.org/pdf/1805.01328v1.pdf

The General Robust Image Task (GRIT) Benchmark is an evaluation-only benchmark for evaluating the performance and robustness of vision systems across multiple image prediction tasks, concepts, and data sources. GRIT hopes to encourage our research community to pursue the following research directions: 1. General purpose vision models - GRIT facilitates the evaluation of unified and general-purpose vision models that demonstrate a wide range of skills across a diverse set of concepts. 2. Robust specialized models - GRIT simplifies and unifies quantification of misinformation, calibration, and generalization under distribution shifts due to novel concepts, novel data sources or image distortions for 7 standard vision and vision-language tasks. 3. Efficient learning - GRIT includes a restricted and an unrestricted track. The restricted track constrains the allowed training data to a selected but rich set of data sources that allows more scientific and meaningful comparison between models. This is meant to encourage resource constrained researchers to participate in the GRIT challenge and to spur interest in efficient learning methods as opposed to the dominant paradigm of training larger models on ever increasing amounts of training data. The unrestricted track allows much more flexibility in training data selection to test the capability of vision models trained with massive data and compute.

We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark. Recent advances in inverse rendering have enabled a wide range of real-world applications in 3D content generation, moving rapidly from research and commercial use cases to consumer devices. While the results continue to improve, there is no real-world benchmark that can quantitatively assess and compare the performance of various inverse rendering methods. Existing real-world datasets typically only consist of the shape and multi-view images of objects, which are not sufficient for evaluating the quality of material recovery and object relighting. Methods capable of recovering material and lighting often resort to synthetic data for quantitative evaluation, which on the other hand does not guarantee generalization to complex real-world environments. We introduce a new dataset of real-world objects captured under a variety of natural scenes with ground-truth 3D scans, multi-view images, and environment lighting. Using this dataset, we establish the first comprehensive real-world evaluation benchmark for object inverse rendering tasks from in-the-wild scenes, and compare the performance of various existing methods.

Provides a large-scale synthetic dataset which contains accurate ground truth depth of various photo-realistic scenes. Source: 3D Ken Burns Effect from a Single Image

Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. The Pano3D 360 depth estimation benchmark provides a standard Matterport3D train and test split, as well as a secondary GibsonV2 partioning for testing and training as well. The latter is used for zero-shot cross dataset transfer performance assessment and decomposes it into 3 different splits, each one focusing on a specific generalization axis.

SuperCaustics is a simulation tool made in Unreal Engine for generating massive computer vision datasets that include transparent objects. SuperCaustics is a real-time, open-source simulation of transparent objects designed for deep learning applications. SuperCaustics features extensive modules for stochastic environment creation; uses hardware ray-tracing to support caustics, dispersion, and refraction; and enables generating massive datasets with multi-modal, pixel-perfect ground truth annotations. <p align="center"> <img src="https://github.com/MMehdiMousavi/SuperCaustics/raw/main/Assets/SuperCaustics.gif" alt="drawing" width="600"/> </p>

50K synthetic renders of the human foot, with surface normals, masks and keypoints.

The dataset contains procedurally generated images of transparent vessels containing liquid and objects . The data for each image includes segmentation maps, 3d depth maps, and normal maps of of the liquid or object inside the transparent vessel, and the vessel. In addition, the properties of the materials inside the containers are given(color/transparency/roughness/metalness). In addition, a natural image benchmark for the 3d/depth estimation of objects inside transparent containers is supplied. 3d models of the objects (GTLF) are also supplied.