Noah Stier

I'm a PhD student in computer science at UCSB, advised by Tobias Höllerer. My research focuses on 3D scene understanding and reconstruction, with applications in robotics, augmented and virtual reality, and more.

I earned a B.S. from UCLA in computational and systems biology, where I worked with Fabien Scalzo to develop computational methods for medical image analysis.

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Research
Weakly-Supervised Convolutional Neural Networks for Vessel Segmentation in Cerebral Angiography
Arvind Vepa, Andrew Choi, Noor Nakhaei, Wonjun Lee, Noah Stier, Andrew Vu, Greyson Jenkins, Xiaoyan Yang, Manjot Shergill, Moira Desphy, Kevin Delao, Mia Levy, Cristopher Garduno, Lacy Nelson, Wandi Liu, Fan Hung, Fabien Scalzo
WACV, 2022

We present a high-quality, annotated DSA vessel segmentation dataset, the largest to-date that is publicly available. We identify semi-automated annotation methods that significantly reduce the labeling cost while preserving the ability to train accurate segmentation models.

VoRTX: Volumetric 3D Reconstruction with Transformers for Voxel-wise View Selection and Fusion
Noah Stier, Alexander Rich, Pradeep Sen, Tobias Höllerer
3DV, 2021 (Oral Presentation)
project page / arXiv

We introduce a 3D reconstruction model with a novel multi-view fusion method based on transformers. It models occlusion by predicting projective occupancy, which reduces noise and leads to more detailed and complete reconstructions.

3DVNet: Multi-View Depth Prediction and Volumetric Refinement
Alexander Rich, Noah Stier, Pradeep Sen, Tobias Höllerer
3DV, 2021

We introduce a deep multi-view stereo network that jointly models all depth maps in scene space, allowing it to learn geometric priors across entire scenes. It iteratively refines its predictions, leading to highly coherent reconstructions.

DeepOSM-3D: Recognition in Aerial LiDAR RGBD Imagery
Abhejit Rajagopal, Noah Stier, William Nelson, Shivkumar Chandrasekaran,
Andrew P Brown
SPIE DCS, 2020

We present a framework for wide-area detection and recognition in co-incident aerial RGB images and LiDAR, and we source ground-truth semantic labels from OpenStreetMap.

Towards Deep Iterative-Reconstruction Algorithms for Computed Tomography (CT) Applications
Abhejit Rajagopal, Noah Stier, Joyoni Dey, Michael A King, Shivkumar Chandrasekaran
SPIE Medical Imaging (MI), 2019

We propose deep neural networks for CT that are equivalent to classical methods at initialization, but can be trained to optimize performance while maintaining convergence properties and interpretability.

Deep Learning of Tissue Fate Features in Acute Ischemic Stroke
Noah Stier, Nicholas Vincent, David S Liebeskind, Fabien Scalzo
IEEE Bioinformatics and Biomedicine (BIBM), 2015

We predict the extent of tissue damage following stroke, using the cerebral hypoperfusion feature observed in MRI after stroke onset. We show improved performance using a CNN vs. the standard single-voxel regression model.

Detection of Hyperperfusion on Arterial Spin Labeling Using Deep Learning
Nicholas Vincent, Noah Stier, Songlin Yu, David S Liebeskind, Danny JJ Wang, Fabien Scalzo
IEEE Bioinformatics and Biomedicine (BIBM), 2015

We train a CNN to classify regions of hyperperfusion based on cerebral blood flow (CBF) observed in MRI after stroke onset, approaching the accuracy of human experts.


Website source code from Jon Barron.