5 papers
PRISM: A 3D Probabilistic Neural Representation for Interpretable Shape Modeling
Yining Jiao, Sreekalyani Bhamidi, Carlton Jude Zdanski +8
Understanding how anatomical shapes evolve in response to developmental covariates - and quantifying their spatially varying uncertainties - is critical in healthcare research. Exi…
LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations
Yining Jiao, Sreekalyani Bhamidi, Huaizhi Qu +11
The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provid…
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
Yining Jiao, Carlton Zdanski, Julia Kimbell +8
Deep implicit functions (DIFs) have emerged as a powerful paradigm for many computer vision tasks such as 3D shape reconstruction, generation, registration, completion, editing, an…
VPFusion: Joint 3D Volume and Pixel-Aligned Feature Fusion for Single and Multi-view 3D Reconstruction
Jisan Mahmud, Jan-Michael Frahm
We introduce a unified single and multi-view neural implicit 3D reconstruction framework VPFusion. VPFusion attains high-quality reconstruction using both - 3D feature volume to ca…
ViewSynth: Learning Local Features from Depth using View Synthesis
Jisan Mahmud, Rajat Vikram Singh, Peri Akiva +3
The rapid development of inexpensive commodity depth sensors has made keypoint detection and matching in the depth image modality an important problem in computer vision. Despite g…