FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction-Reference-Cited by-同舟云学术

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FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction

Published:2023-10-01 Issue: Volume:34 Page:18377-18386
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
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Author:

Stier Noah¹,Ranjan Anurag¹,Colburn Alex¹,Yan Yajie¹,Yang Liang¹,Ma Fangchang¹,Angles Baptiste¹

Affiliation:

1. Apple

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10378160.pdf?arnumber=10378160

Reference31 articles.

1. Transformerfusion: Monocular rgb scene reconstruction using transformers;Božič;Proc. Neural Information Processing Systems (NeurIPS),2021

2. VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction

3. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

4. A volumetric method for building complex models from range images

5. ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

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1. Depth Prior-Guided 3D Voxel Feature Fusion for 3D Semantic Estimation from Monocular Videos;Mathematics;2024-07-05

2. Effect of Encoder Architectures on the Generation of Vector Representations for Modeling 3D Objects via the Space of Convex Sets;2024 X International Conference on Information Technology and Nanotechnology (ITNT);2024-05-20

3. PIXGAN-Drone: 3D Avatar of Human Body Reconstruction From Multi-View 2D Images;IEEE Access;2024

4. TerrainMesh: Metric-Semantic Terrain Reconstruction From Aerial Images Using Joint 2-D-3-D Learning;IEEE Transactions on Robotics;2024

5. DG-Recon: Depth-Guided Neural 3D Scene Reconstruction;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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