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Found 12 papers, 2 papers with code
Divide and Conquer: Rethinking the Training Paradigm of Neural Radiance Fields
Neural radiance fields (NeRFs) have exhibited potential in synthesizing high-fidelity views of 3D scenes but the standard training paradigm of NeRF presupposes an equal importance for each image in the training set.
Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis
Wound management poses a significant challenge, particularly for bedridden patients and the elderly.
CorticalFlow$^{++}$: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability
Using the state-of-the-art CorticalFlow model as a blueprint, this paper proposes three modifications to improve its accuracy and interoperability with existing surface analysis tools, while not sacrificing its fast inference time and low GPU memory consumption.
CorticalFlow: A Diffeomorphic Mesh Deformation Module for Cortical Surface Reconstruction
In this paper we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object.
CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction
In this paper, we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object.
MongeNet: Efficient Sampler for Geometric Deep Learning
Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes.
DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction
Having these limitations in mind, we propose DeepCSR, a 3D deep learning framework for cortical surface reconstruction from MRI.
Going deeper with brain morphometry using neural networks
Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases.
Inferring Temporal Compositions of Actions Using Probabilistic Automata
This paper presents a framework to recognize temporal compositions of atomic actions in videos.
Neural Algebra of Classifiers
In this paper, we build on the compositionality principle and develop an "algebra" to compose classifiers for complex visual concepts.
DeepPermNet: Visual Permutation Learning
Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet, an end-to-end CNN model for this task.
On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization
Some recent works in machine learning and computer vision involve the solution of a bi-level optimization problem.
