3D Shape Reconstruction
58 papers with code • 2 benchmarks • 8 datasets
Most implemented papers
3D Reconstruction of Novel Object Shapes from Single Images
This is challenging as it requires a model to learn a representation that can infer both the visible and occluded portions of any object using a limited training set.
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning
3D human shape and pose estimation from monocular images has been an active area of research in computer vision, having a substantial impact on the development of new applications, from activity recognition to creating virtual avatars.
Active 3D Shape Reconstruction from Vision and Touch
In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2)a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration.
ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization
A novel disentangled shape and appearance database of priors is first learned to embed objects in their respective shape and appearance space.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
We study 3D shape modeling from a single image and make contributions to it in three aspects.
Domain-Adaptive Single-View 3D Reconstruction
In this paper, we propose a framework to improve over these challenges using adversarial training.
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object.
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances.
Deep Meta Functionals for Shape Representation
We present a new method for 3D shape reconstruction from a single image, in which a deep neural network directly maps an image to a vector of network weights.