3D Reconstruction

600 papers with code • 8 benchmarks • 55 datasets

3D Reconstruction is the task of creating a 3D model or representation of an object or scene from 2D images or other data sources. The goal of 3D reconstruction is to create a virtual representation of an object or scene that can be used for a variety of purposes, such as visualization, animation, simulation, and analysis. It can be used in fields such as computer vision, robotics, and virtual reality.

Image: Gwak et al

Most implemented papers

Instant Neural Graphics Primitives with a Multiresolution Hash Encoding

nvlabs/instant-ngp 16 Jan 2022

Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate.

3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction

chrischoy/3D-R2N2 2 Apr 2016

Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2).

Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping

MIT-SPARK/Kimera 6 Oct 2019

We provide an open-source C++ library for real-time metric-semantic visual-inertial Simultaneous Localization And Mapping (SLAM).

The Double Sphere Camera Model

ethz-asl/kalibr 24 Jul 2018

We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i. e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians.

Occupancy Networks: Learning 3D Reconstruction in Function Space

LMescheder/Occupancy-Networks CVPR 2019

With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity.

PCRNet: Point Cloud Registration Network using PointNet Encoding

vinits5/learning3d 21 Aug 2019

PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation and shape completion.

Convolutional Occupancy Networks

autonomousvision/convolutional_occupancy_networks ECCV 2020

Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction.

EPP-MVSNet: Epipolar-Assembling Based Depth Prediction for Multi-View Stereo

mindspore-ai/models ICCV 2021

As a result, we achieve promising results on all datasets and the highest F-Score on the online TNT intermediate benchmark.

DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation

Facebookresearch/deepsdf CVPR 2019

In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data.

Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images

hzxie/Pix2Vox ICCV 2019

Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e. g., table legs) from different coarse 3D volumes to obtain a fused 3D volume.