5 citations · 5 across the 2 of their papers we have counts for
4 papers
ProDyG: Progressive Dynamic Scene Reconstruction via Gaussian Splatting from Monocular Videos
Shi Chen, Erik Sandström, Sandro Lombardi +2
Achieving truly practical dynamic 3D reconstruction requires online operation, global pose and map consistency, detailed appearance modeling, and the flexibility to handle both RGB…
NeuralMeshing: Differentiable Meshing of Implicit Neural Representations
Mathias Vetsch, Sandro Lombardi, Marc Pollefeys +1
The generation of triangle meshes from point clouds, i.e. meshing, is a core task in computer graphics and computer vision. Traditional techniques directly construct a surface mesh…
LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies
Sandro Lombardi, Bangbang Yang, Tianxing Fan +4
3D representation and reconstruction of human bodies have been studied for a long time in computer vision. Traditional methods rely mostly on parametric statistical linear models,…
Self-Supervised Learning of Non-Rigid Residual Flow and Ego-Motion
Ivan Tishchenko, Sandro Lombardi, Martin R. Oswald +1
Most of the current scene flow methods choose to model scene flow as a per point translation vector without differentiating between static and dynamic components of 3D motion. In t…