What is a 3D dataset?
What is a 3D dataset?
Structured3D is a large-scale photo-realistic dataset containing 3.5K house designs (a) created by professional designers with a variety of ground truth 3D structure annotations (b) and generate photo-realistic 2D images (c).
Where can I find 3D datasets?
3D Model/Shape Retrieval Datasets
- ShapeNet Dataset. The ShapeNetCore covers 55 common object categories with about 51,300 unique 3D models.
- ModelNet Dataset.
- UWA Dataset.
- Princeton Shape Benchmark.
- Colored 3D Model Database.
- SHREC10 Datasets.
- TOSCA High-Resolution.
- Non-Rigid World.
What is Objectron dataset?
The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment.
What is 3D object reconstruction?
In computer vision and computer graphics, 3D reconstruction is the process of capturing the shape and appearance of real objects. This process can be accomplished either by active or passive methods. If the model is allowed to change its shape in time, this is referred to as non-rigid or spatio-temporal reconstruction.
How would you describe a 3D object?
What are Three-Dimensional shapes? In geometry, a three-dimensional shape can be defined as a solid figure or an object or shape that has three dimensions – length, width and height. Unlike two-dimensional shapes, three-dimensional shapes have thickness or depth.
How do you represent a 3D object?
The most commonly used boundary representation for a 3D graphics object is a set of surface polygons that enclose the object interior. Many graphics system use this method. Set of polygons are stored for object description.
How do I download a Shapeset dataset?
ShapeNet is a dataset of 3D CAD models. ShapeNetCore is a subset of the ShapeNet dataset and can be downloaded from https://www.shapenet.org/.
What is MediaPipe?
MediaPipe is a cross-platform framework for building multimodal applied machine learning pipelines. MediaPipe is a framework for building multimodal (eg. video, audio, any time series data), cross platform (i.e Android, iOS, web, edge devices) applied ML pipelines.
What is monocular 3D object detection?
Specifically, the proposed framework predicts camera extrinsic parameters by detecting vanishing point and horizon change. A converter is designed to rectify perturbative features in the latent space.
Where is 3D reconstruction used?
3D reconstruction is used in the medical field through medical imaging equipment. This input data can then be used to create a 3D reconstruction of the original object or objects. A scan of a person’s body, for example, can be used to create a 3D model of that person in a computer system.
Why is 3D reconstruction important?
Three-dimensional object reconstruction Reconstruction allows us to gain insight into qualitative features of the object which cannot be deduced from a single plane of sight, such as volume and the object relative position to others in the scene.
What is the best dataset for 3D object recognition?
In this article, we present one large-scale dataset, ObjectNet3D, and also several specialized datasets for 3D object recognition: MVTec ITODD and T-LESS – for industry settings and Falling Things dataset – for object recognition tasks in the context of robotics.
What can I do with the resulting dataset?
The resulting dataset can be used for object proposal generation, 2D object detection, joint 2D detection and 3D object pose estimation, image-based 3D shape retrieval. Example scene of the dataset from all sensors. Top row: grayscale cameras. Bottom row: Z and grayscale image of the High-Quality (left) and Low-Quality (right) 3D sensor
What is the itodd dataset?
MVTec ITODD is a dataset for 3D object detection and pose estimation with a strong focus on industrial settings and applications. It contains 28 objects arranged in over 800 scenes and labeled with their rigid 3D transformation as ground truth.
What is fat dataset?
The Falling Things (FAT) dataset is a synthetic dataset for 3D object detection and pose estimation, created by NVIDIA team. It was generated by placing 3D household object models (e.g., mustard bottle, soup can, gelatin box, etc.) in virtual environments.
