53 citations · 59 across the 5 of their papers we have counts for
6 papers
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…
VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams
Nissim Maruani, Roman Klokov, Maks Ovsjanikov +2
In stark contrast to the case of images, finding a concise, learnable discrete representation of 3D surfaces remains a challenge. In particular, while polygon meshes are arguably t…
Discrete Point Flow Networks for Efficient Point Cloud Generation
Roman Klokov, Edmond Boyer, Jakob Verbeek
Generative models have proven effective at modeling 3D shapes and their statistical variations. In this paper we investigate their application to point clouds, a 3D shape represent…
Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image
Roman Klokov, Jakob Verbeek, Edmond Boyer
We study end-to-end learning strategies for 3D shape inference from images, in particular from a single image. Several approaches in this direction have been investigated that expl…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
Roman Klokov, Victor Lempitsky
We present a new deep learning architecture (called Kd-network) that is designed for 3D model recognition tasks and works with unstructured point clouds. The new architecture perfo…