3D Scene Reconstruction
54 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
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
zju3dv/NeuralRecon
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CVPR 2021
We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video.
The Replica Dataset: A Digital Replica of Indoor Spaces
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
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ECCV 2020
Furthermore, we adapt our model to address the harder task of reconstructing multiple objects from a single image.
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.
SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields
astra-vision/SceneRF
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ICCV 2023
3D reconstruction from a single 2D image was extensively covered in the literature but relies on depth supervision at training time, which limits its applicability.
UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous Driving
When compared to monocular pre-training methods on the nuScenes dataset, UniScene shows a significant improvement of about 2. 0% in mAP and 2. 0% in NDS for multi-camera 3D object detection, as well as a 3% increase in mIoU for surrounding semantic scene completion.
HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans
stefan-ainetter/SCANnotateDataset
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We present an automated and efficient approach for retrieving high-quality CAD models of objects and their poses in a scene captured by a moving RGB-D camera.
DeepV2D: Video to Depth with Differentiable Structure from Motion
princeton-vl/DeepV2D
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ICLR 2020
We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video.
Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera
Depth sensing is crucial for 3D reconstruction and scene understanding.
Atlas: End-to-End 3D Scene Reconstruction from Posed Images
Traditional approaches to 3D reconstruction rely on an intermediate representation of depth maps prior to estimating a full 3D model of a scene.
