7 citations · 9 across the 4 of their papers we have counts for
6 papers
PatchAlign3D: Local Feature Alignment for Dense 3D Shape Understanding
Souhail Hadgi, Bingchen Gong, Ramana Sundararaman +4
Current foundation models for 3D shapes excel at global tasks (retrieval, classification) but transfer poorly to local part-level reasoning. Recent approaches leverage vision and l…
Deformation Recovery: Localized Learning for Detail-Preserving Deformations
Ramana Sundararaman, Nicolas Donati, Simone Melzi +2
We introduce a novel data-driven approach aimed at designing high-quality shape deformations based on a coarse localized input signal. Unlike previous data-driven methods that requ…
Self-Supervised Dual Contouring
Ramana Sundararaman, Roman Klokov, Maks Ovsjanikov
Learning-based isosurface extraction methods have recently emerged as a robust and efficient alternative to axiomatic techniques. However, the vast majority of such approaches rely…
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching
Ramana Sundararaman, Riccardo Marin, Emanuele Rodola +1
In this work we present a novel approach for computing correspondences between non-rigid objects, by exploiting a reduced representation of deformation fields. Different from exist…
Implicit field supervision for robust non-rigid shape matching
Ramana Sundararaman, Gautam Pai, Maks Ovsjanikov
Establishing a correspondence between two non-rigidly deforming shapes is one of the most fundamental problems in visual computing. Existing methods often show weak resilience when…
Tracking Pedestrian Heads in Dense Crowd
Ramana Sundararaman, Cedric De Almeida Braga, Eric Marchand +1
Tracking humans in crowded video sequences is an important constituent of visual scene understanding. Increasing crowd density challenges visibility of humans, limiting the scalabi…