5 papers
A Persistent Homology Design Space for 3D Point Cloud Deep Learning
Prachi Kudeshia, Jiju Poovvancheri, Amr Ghoneim +1
Persistent Homology (PH) offers stable, multi-scale descriptors of intrinsic shape structure by capturing connected components, loops, and voids that persist across scales, providi…
Learning Significant Persistent Homology Features for 3D Shape Understanding
Prachi Kudeshia, Jiju Poovvancheri
Geometry and topology constitute complementary descriptors of three-dimensional shape, yet existing benchmark datasets primarily capture geometric information while neglecting topo…
DepGAN: Leveraging Depth Maps for Handling Occlusions and Transparency in Image Composition
Amr Ghoneim, Jiju Poovvancheri, Yasushi Akiyama +1
Image composition is a complex task which requires a lot of information about the scene for an accurate and realistic composition, such as perspective, lighting, shadows, occlusion…
Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud
Ayumu Saito, Prachi Kudeshia, Jiju Poovvancheri
Recent advancements in self-supervised learning in the point cloud domain have demonstrated significant potential. However, these methods often suffer from drawbacks, including len…
RBF Weighted Hyper-Involution for RGB-D Object Detection
Mehfuz A Rahman, Khushal Das, Jiju Poovvancheri +2
A vast majority of augmented reality devices come equipped with depth and color cameras. Despite their advantages, extracting both photometric and depth features simultaneously in…