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portfolio
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publications
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
Published in International Conference on Computer Vision, 2017
The paper presents a novel neural architecture for feature extraction from point clouds based on hierarchical feature pooling from kd-trees constructed on the input point clouds. The architecture is evaluated in several point cloud recognition applications including classification, segmentation and retrieval.
R. Klokov and V. Lempitsky. "Escape from cells: Deep kd-networks for the recognition of 3d point cloud models." In ICCV'17.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Published in International Conference on Computer Vision Workshop, 2017
The paper presents the results of the ShapeNet point cloud segmentation and single-view reconstruction challenges held at ICCV'17 Workshop. Our segmentation architecture took second place.
L. Yi, et at. "Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55." In ICCVW'17.
Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image
Published in British Machine Vision Conference, 2019
The paper analyses learning strategies for the single-view reconstruction task. The problem is framed as a conditional generation task with image-conditioned prior. Various learning strategies and image conditioning mechanisms are compared to show that variational training scheme with shape-conditioned posterior and image-conditined prior distributions for the global latent variable.
This work recieved a Best Science Paper Honorable Mention Award.
R. Klokov, J. Verbeek and E. Boyer. "Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image." In BMVC'19.
Discrete Point Flow Networks for Efficient Point Cloud Generation
Published in European Conference on Computer Vision, 2020
The paper proposes a novel hierarchical probabilistic generative model for point clouds based on invertible conditional affine coupling flows reimagined for point clouds, and capable of generation of variable size point clouds. The model was adapted to single-view reconstruction task and demonstrated performance similar to the state of the art while being two magnitudes faster during training and sampling.
R. Klokov, E. Boyer and J. Verbeek. "Discrete Point Flow Networks for Efficient Point Cloud Generation." In ECCV'20.
VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams
Published in International Conference on Computer Vision, 2023
The paper proposes a novel differentiable resperentation for surfaces based on Voronoi diagrams with direct access to watertight mesh extraction. VoroMesh is verified in two settings: direct per-object optimization (overfitting), and infernce-based mesh reconstuction from low-resolution grids of signed distance function values.
N. Maruani, R. Klokov, M. Ovsjanikov, P. Alliez and M. Desbrun. "VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams." In ICCV'23.
Self-Supervised Dual Contouring
Published in Conference on Computer Vision and Pattern Recognition, 2024
The paper proposes a novel training strategy for neural dual contouring differentiable meshing model based on establishing consistency between input ground truth signed distance function (SDF) values/normals and SDF values/normals to the predicted mesh. The method can additionally be used to regularize predictions of neural SDF models.
R. Sundararaman, R. Klokov and M. Ovsjanikov. "Self-Supervised Dual Contouring." In CVPR'24.
talks
Escape from Cells, Spotlight at ICCV’17
Published:
Spotlight presentation of the paper «Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models» at the International Conference on Computer Vision 2017.
Probabilistic Reconstruction Networks, Oral at BMVC’19
Published:
Oral presentation of the paper «Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image» at the British Machine Vision Conference 2019.
Probabilistic Reconstruction Networks, Yandex’19
Published:
Invited talk about the paper «Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image» at the Fifth Christmas Colloquium on Computer Vision at Yandex, 2019.
Discrete Point Flow Networks, Online Presentation at ECCV’20
Published:
Online presentation of the paper «Discrete Point Flow Networks for Efficient Point Cloud Generation» at the European Conference on Computer Vision 2020.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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