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adversarial-10

3D Architecture

3260 papers • 126 benchmarks • 313 datasets

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petteriTeikari/vesselNN

2 papers 12

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Most implemented papers

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection

Kisuk Lee, H. Seung, A. Zlateski, Ashwin Vishwanathan•Wed Aug 19 2015

This work adopts a recursively trained architecture in which a first network generates a preliminary boundary map that is provided as input along with the original image to a second network that generates a final boundary map, and uses a much deeper network than previously employed for boundary detection.

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Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation

Petteri Teikari, Marc A. Santos, Charissa Poon, K. Hynynen•Tue Jun 07 2016

This work provided a freely available dataset of 12 annotated two-photon vasculature microscopy stacks and demonstrated the use of deep learning framework consisting both 2D and 3D convolutional filters (ConvNet) that produced promising segmentation result.

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3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation

Angela Dai, M. Nießner•Tue Mar 27 2018

3DMV is presented, a novel method for 3D semantic scene segmentation of RGB-D scans in indoor environments using a joint 3D-multi-view prediction network that achieves significantly better results than existing baselines.

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DR-Unet104 for Multimodal MRI brain tumor segmentation

Wenting Duan, Jordan Colman, Lei Zhang, Xujiong Ye•Tue Nov 03 2020

A 2D deep residual Unet with 104 convolutional layers (DR-Unet104) for lesion segmentation in brain MRIs is presented as a state-of-the-art 2D lesions segmentation architecture that can be used on lower power computers than a 3D architecture.

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Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video Classification

Kimin Yun, Youngwan Lee, Hyungil Kim, Jinyoung Moon•Mon Nov 30 2020

A new 3D backbone network, called VoV3D, is proposed that consists of a temporal one-shot aggregation (T-OSA) module and a depthwise factorized component, D(2+1)D, that decomposes a 3D depthwise Convolution into two spatial and temporal depthwise convolutions for efficient architecture.

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ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding

S. Savarese, Jiajun Wu, Juan Carlos Niebles, Caiming Xiong, Ran Xu, Roberto Mart'in-Mart'in, Le Xue, Mingfei Gao, Chen Xing•Fri Dec 09 2022

ULIP is introduced to learn a unified representation of image, text, and 3D point cloud by pre-training with object triplets from the three modalities, using a pre-trained vision-language model that has already learned a common visual and textual space by training with massive image-text pairs.

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Improved distinct bone segmentation in upper-body CT through multi-resolution networks

Julia Wolleb, P. Cattin, Eva Schnider, Antal Huck, M. Toranelli, G. Rauter, M. Müller-Gerbl•Mon Jan 30 2023

Multi-resolution 3D U-Nets address current shortcomings in bone segmentation from upper-body CT scans by allowing for capturing a larger field of view while avoiding the cubic growth of the input pixels and intermediate computations that quickly outgrow the computational capacities in 3D.

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PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters

X. Yang, Shuhong Chen, Matthias Zwicker, Yichun Shi, Kevin Zhang, Heng Wang, Guoxian Song, Sizhe An, Janus Kristjansson•Fri Mar 24 2023

PAniC-3D significantly outper-forms baseline methods, and provides data to establish the task of stylized reconstruction from portrait illustrations, and represents sophisticated geometries with a volumetric radiance field.

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3D Architecture | State-of-the-Art