Multi-View 3D Reconstruction
25 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Multi-View 3D Reconstruction
Synthetic Soccer NeRF Dataset
Most implemented papers
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
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.
Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction
Yang7879/AttSets
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However, GRU based approaches are unable to consistently estimate 3D shapes given different permutations of the same set of input images as the recurrent unit is permutation variant.
SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
YueJiang-nj/CVPR2020-SDFDiff
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CVPR 2020
We propose SDFDiff, a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDFs).
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
autonomousvision/differentiable_volumetric_rendering
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CVPR 2020
In this work, we propose a differentiable rendering formulation for implicit shape and texture representations.
Extremely Dense Point Correspondences using a Learned Feature Descriptor
lppllppl920/DenseDescriptorLearning-Pytorch
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CVPR 2020
In direct comparison to recent local and dense descriptors on an in-house sinus endoscopy dataset, we demonstrate that our proposed dense descriptor can generalize to unseen patients and scopes, thereby largely improving the performance of Structure from Motion (SfM) in terms of model density and completeness.
Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance
za-cheng/PM-PMVS
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CVPR 2021
Recovering the 3D geometry of a purely texture-less object with generally unknown surface reflectance (e. g. non-Lambertian) is regarded as a challenging task in multi-view reconstruction.
LegoFormer: Transformers for Block-by-Block Multi-view 3D Reconstruction
faridyagubbayli/LegoFormer
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Most modern deep learning-based multi-view 3D reconstruction techniques use RNNs or fusion modules to combine information from multiple images after independently encoding them.
H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction
In this paper, we tackle these limitations for the specific problem of few-shot full 3D head reconstruction, by endowing coordinate-based representations with a probabilistic shape prior that enables faster convergence and better generalization when using few input images (down to three).
3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers
3D-RETR is capable of 3D reconstruction from a single view or multiple views.
Improving neural implicit surfaces geometry with patch warping
fdarmon/neuralwarp
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CVPR 2022
Neural implicit surfaces have become an important technique for multi-view 3D reconstruction but their accuracy remains limited.
