3D Scene Reconstruction
40 papers with code • 0 benchmarks • 3 datasets
Creating 3D scene either using conventional SFM pipelines or latest deep learning approaches.
Benchmarks
These leaderboards are used to track progress in 3D Scene Reconstruction
IBISCape
Most implemented papers
Learning to Recover 3D Scene Shape from a Single Image
aim-uofa/AdelaiDepth
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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.
A Pose-only Solution to Visual Reconstruction and Navigation
Visual navigation and three-dimensional (3D) scene reconstruction are essential for robotics to interact with the surrounding environment.
RetrievalFuse: Neural 3D Scene Reconstruction with a Database
nihalsid/retrieval-fuse
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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.
TransformerFusion: Monocular RGB Scene Reconstruction using Transformers
aljazbozic/transformerfusion
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NeurIPS 2021
We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach.
Panoptic 3D Scene Reconstruction From a Single RGB Image
xheon/panoptic-reconstruction
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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.
Deblur-NeRF: Neural Radiance Fields from Blurry Images
limacv/Deblur-NeRF
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CVPR 2022
We demonstrate that our method can be used on both camera motion blur and defocus blur: the two most common types of blur in real scenes.
MonoScene: Monocular 3D Semantic Scene Completion
cv-rits/MonoScene
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CVPR 2022
MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense geometry and semantics of a scene are inferred from a single monocular RGB image.
Neural 3D Scene Reconstruction with the Manhattan-world Assumption
zju3dv/manhattan_sdf
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CVPR 2022
Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network.
READ: Large-Scale Neural Scene Rendering for Autonomous Driving
JOP-Lee/READ-Large-Scale-Neural-Scene-Rendering-for-Autonomous-Driving
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In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to synthesize large-scale driving scenarios on a PC through a variety of sampling schemes.
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes
vilab-ucsd/photoscene
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CVPR 2022
Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout.
