3D Scene Reconstruction

15 papers with code • 1 benchmarks • 1 datasets

Creating 3D scene either using conventional SFM pipelines or latest deep learning approaches.

Most implemented papers

The Replica Dataset: A Digital Replica of Indoor Spaces

facebookresearch/Replica-Dataset 13 Jun 2019

We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale.

CoReNet: Coherent 3D scene reconstruction from a single RGB image

google-research/corenet ECCV 2020

Furthermore, we adapt our model to address the harder task of reconstructing multiple objects from a single image.

Consistent Generative Query Networks

rnagumo/gqnlib ICLR 2019

These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.

Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera

NVlabs/neuralrgbd 9 Jan 2019

Depth sensing is crucial for 3D reconstruction and scene understanding.

Atlas: End-to-End 3D Scene Reconstruction from Posed Images

magicleap/Atlas ECCV 2020

Traditional approaches to 3D reconstruction rely on an intermediate representation of depth maps prior to estimating a full 3D model of a scene.

GRF: Learning a General Radiance Field for 3D Representation and Rendering

alextrevithick/GRF ICCV 2021

We present a simple yet powerful neural network that implicitly represents and renders 3D objects and scenes only from 2D observations.

Learning to Recover 3D Scene Shape from a Single Image

aim-uofa/AdelaiDepth CVPR 2021

Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length.

RetrievalFuse: Neural 3D Scene Reconstruction with a Database

nihalsid/retrieval-fuse ICCV 2021

3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks.

NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video

zju3dv/NeuralRecon CVPR 2021

We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video.

Panoptic 3D Scene Reconstruction From a Single RGB Image

xheon/panoptic-reconstruction NeurIPS 2021

Inspired by 2D panoptic segmentation, we propose to unify the tasks of geometric reconstruction, 3D semantic segmentation, and 3D instance segmentation into the task of panoptic 3D scene reconstruction - from a single RGB image, predicting the complete geometric reconstruction of the scene in the camera frustum of the image, along with semantic and instance segmentations.