3D Shape Reconstruction

58 papers with code • 2 benchmarks • 8 datasets

Most implemented papers

3D Reconstruction of Novel Object Shapes from Single Images

rehg-lab/3dshapegen 14 Jun 2020

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

xuxy09/RSC-Net ECCV 2020

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

facebookresearch/Active-3D-Vision-and-Touch NeurIPS 2021

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

zubair-irshad/shapo 27 Jul 2022

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

facebookresearch/SparseConvNet 17 Oct 2017

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

xingyuansun/pix3d CVPR 2018

We study 3D shape modeling from a single image and make contributions to it in three aspects.

Domain-Adaptive Single-View 3D Reconstruction

Gitikameher/Domain-Adaptive-Single-View-3D-Reconstruction ICCV 2019

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

shunsukesaito/PIFu ICCV 2019

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

drsrinathsridhar/xnocs NeurIPS 2019

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

gidilittwin/Deep-Meta ICCV 2019

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.