Single-View 3D Reconstruction

The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions. Source: Fine-grained Visual-textual Representation Learning Image Source: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html

ShapeNet is a large scale repository for 3D CAD models developed by researchers from Stanford University, Princeton University and the Toyota Technological Institute at Chicago, USA. The repository contains over 300M models with 220,000 classified into 3,135 classes arranged using WordNet hypernym-hyponym relationships. ShapeNet Parts subset contains 31,693 meshes categorised into 16 common object classes (i.e. table, chair, plane etc.). Each shapes ground truth contains 2-5 parts (with a total of 50 part classes). Source: A review on deep learning techniques for 3D sensed data classification Image Source: ShapeNet: An Information-Rich 3D Model Repository

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

SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation. It is derived from the KITTI Vision Odometry Benchmark which it extends with dense point-wise annotations for the complete 360 field-of-view of the employed automotive LiDAR. The dataset consists of 22 sequences. Overall, the dataset provides 23201 point clouds for training and 20351 for testing. Source: Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation Image Source: https://github.com/PRBonn/semantic-kitti-api

KITTI-360 is a large-scale dataset that contains rich sensory information and full annotations. It is the successor of the popular KITTI dataset, providing more comprehensive semantic/instance labels in 2D and 3D, richer 360 degree sensory information (fisheye images and pushbroom laser scans), very accurate and geo-localized vehicle and camera poses, and a series of new challenging benchmarks.

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.

For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. Hypersim is a photorealistic synthetic dataset for holistic indoor scene understanding. It contains 77,400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry. Source: https://github.com/apple/ml-hypersim Image Source: https://github.com/apple/ml-hypersim

The ABC Dataset is a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms.

Scanned Objects by Google Research is a dataset of common household objects that have been 3D scanned for use in robotic simulation and synthetic perception research.

ABO is a large-scale dataset designed for material prediction and multi-view retrieval experiments. The dataset contains Blender renderings of 30 viewpoints for each of the 7,953 3D objects, as well as camera intrinsics and extrinsic for each rendering. Source: ABO: Dataset and Benchmarks for Real-World 3D Object Understanding Image Source: https://arxiv.org/pdf/2110.06199.pdf

Common Objects in 3D is a large-scale dataset with real multi-view images of object categories annotated with camera poses and ground truth 3D point clouds. The dataset contains a total of 1.5 million frames from nearly 19,000 videos capturing objects from 50 MS-COCO categories and, as such, it is significantly larger than alternatives both in terms of the number of categories and objects. Source: Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction

SceneNet is a dataset of labelled synthetic indoor scenes. There are several labeled indoor scenes, including: - 11 Bedroom scenes with 428 objects - 15 Office scenes with 1,203 objects - 11 Kitchen scenes with 797 objects - 10 Living Room scenes with 715 objects - 10 Bathrooms with 556 objects Source: https://robotvault.bitbucket.io/ Image Source: https://robotvault.bitbucket.io/big_scene.html

SSCBench establishes a large-scale SSC benchmark in street views that facilitates the training of robust and generalizable SSC models. Overall, SSCBench consists of three subsets, including 38,562 frames for training, 15,798 frames for validation, and 12,553 frames for testing respectively, amounting totally to 66,913 frames.

ShapenetRenderer is an extension of the ShapeNet Core dataset which has more variation in camera angles. For each mesh model, the dataset provides 36 views with smaller variation and 36 views with larger variation. The resolution of the newly rendered images is 224x224 in contrast to the 137x137 original resolution. Additionally, each RGB image is paired with a depth image, a normal map and an albedo image. Source: https://github.com/Xharlie/ShapenetRender_more_variation Image Source: https://github.com/Xharlie/ShapenetRender_more_variation

Real-world dataset of ~400 images of cuboid-shaped parcels with full 2D and 3D annotations in the COCO format.

Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain relevant application cases such as endoscopies. To tackle this issue with a common benchmark, we introduce the Drunkard’s Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes where every surface exhibits non-rigid deformations over time. Simulations in realistic 3D buildings lets us obtain a vast amount of data and ground truth labels, including camera poses, RGB images and depth, optical flow and normal maps at high resolution and quality.

Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the COCO format. For details see our paper and for visual samples our project page.

Event cameras are sensors that are inspired by biological systems and specialize in capturing changes in brightness. These emerging cameras offer numerous advantages over conventional frame-based cameras, including high dynamic range, high frame rates, and extremely low power consumption. As a result, event cameras are increasingly being used in various fields, such as object detection and tracking, autonomous driving, 3D reconstruction, visual odometry, and SLAM. We have created the first large-scale synthetic event camera voxel 3D reconstruction dataset, comprising over 39,739 simulated event camera 3D object scans from 13 different object categories. Each entry in the dataset contains a 0.5-second, 240fps high frame rate RGB video scan, simulated event camera data, the original 3D model, and a converted 32x32x32 voxel model. The 3D models used in this dataset are from ShapeNet (Link: https://shapenet.org/). Although this dataset only provides voxel representation for ground truth, obtaining other types of representation such as point cloud as ground truth will be trivial with the provided gltf 3D model. We hope that by publishing this dataset, we can accelerate the advancement of event-based 3D reconstruction. The original paper is available at: IEEE Xplore - https://ieeexplore.ieee.org/document/10169359 ArXiv - https://arxiv.org/abs/2309.00385 !!! Due to limited resources, we are unable to release the full dataset, which is around 1.2 TB in size. We would greatly appreciate any organizations willing to host the full dataset for us. (Contact: haodong.chen@sydney.edu.au) !!! The released SynthEVox3D-Tiny dataset, which is the dataset used in the original paper, is around 32 GB. We also provide scripts in the utils folder of the dataset to reproduce our results, making it possible for other researchers to recreate the entire dataset from scratch.

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.