Sara Rojas

I'm a PhD Candidate at KAUST in Saudi Arabia, under the supervision of Professor Bernard Ghanem.

Experience in neural rendering, 3D reconstruction, 3D-based recognition tasks, diffusion models and robustness.

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Research

I'm interested in 3D computer vision, deep learning, generative AI, and image processing. Most of my research focuses on NeRF and its applications, including scene editing and efficiency.

We introduce an inpainting approach that leverages the depth information of NeRF scenes to distribute 2D edits across different images, ensuring robustness against errors and resampling challenges. .

Re-ReND distills the NeRF by extracting the learned density into a mesh, while the learned color information is factorized into a set of matrices that represent the scene's light field. Re-ReND can achieve over a 2.6-fold increase in rendering speed versus the SOTA.

A neural field representation that integrates a semantic field along with the usual radiance field. SegNeRF inherits from previous works the ability to perform novel view synthesis and 3D reconstruction, and enables 3D part segmentation from a few images.

We perform transferable adversarial attacks on 3D point clouds by utilizing a point cloud autoencoder. We exceed SOTA by up to 40% on transferability and 38% in breaking SOTA 3D defenses on ModelNet40 data.

Kudos to Dr. Jon Barron for sharing his website template.