184 citations · 805 across the 36 of their papers we have counts for
13 papers · 1 filter
Multi-sensor large-scale dataset for multi-view 3D reconstruction
Oleg Voynov, Gleb Bobrovskikh, Pavel Karpyshev +13
We present a new multi-sensor dataset for multi-view 3D surface reconstruction. It includes registered RGB and depth data from sensors of different resolutions and modalities: smar…
Neural Fields as Learnable Kernels for 3D Reconstruction
Francis Williams, Zan Gojcic, Sameh Khamis +4
We present Neural Kernel Fields: a novel method for reconstructing implicit 3D shapes based on a learned kernel ridge regression. Our technique achieves state-of-the-art results wh…
3D Parametric Wireframe Extraction Based on Distance Fields
Albert Matveev, Alexey Artemov, Denis Zorin +1
We present a pipeline for parametric wireframe extraction from densely sampled point clouds. Our approach processes a scalar distance field that represents proximity to the nearest…
Unpaired Depth Super-Resolution in the Wild
Aleksandr Safin, Maxim Kan, Nikita Drobyshev +5
Depth maps captured with commodity sensors are often of low quality and resolution; these maps need to be enhanced to be used in many applications. State-of-the-art data-driven met…
Towards Part-Based Understanding of RGB-D Scans
Alexey Bokhovkin, Vladislav Ishimtsev, Emil Bogomolov +4
Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes; however, a finer-gra…
DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes
Albert Matveev, Ruslan Rakhimov, Alexey Artemov +6
We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes. Differently from existing data-driven method…
CAD-Deform: Deformable Fitting of CAD Models to 3D Scans
Vladislav Ishimtsev, Alexey Bokhovkin, Alexey Artemov +4
Shape retrieval and alignment are a promising avenue towards turning 3D scans into lightweight CAD representations that can be used for content creation such as mobile or AR/VR gam…
Making DensePose fast and light
Ruslan Rakhimov, Emil Bogomolov, Alexandr Notchenko +4
DensePose estimation task is a significant step forward for enhancing user experience computer vision applications ranging from augmented reality to cloth fitting. Existing neural…
Latent Video Transformer
Ruslan Rakhimov, Denis Volkhonskiy, Alexey Artemov +2
The video generation task can be formulated as a prediction of future video frames given some past frames. Recent generative models for videos face the problem of high computationa…
Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks
Francis Williams, Matthew Trager, Joan Bruna +1
We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks. Our method achieves…
Deep Vectorization of Technical Drawings
Vage Egiazarian, Oleg Voynov, Alexey Artemov +5
We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images. Our method includes (1) a deep learning-based…
Perceptual deep depth super-resolution
Oleg Voynov, Alexey Artemov, Vage Egiazarian +4
RGBD images, combining high-resolution color and lower-resolution depth from various types of depth sensors, are increasingly common. One can significantly improve the resolution o…
Deep Geometric Prior for Surface Reconstruction
Francis Williams, Teseo Schneider, Claudio Silva +3
The reconstruction of a discrete surface from a point cloud is a fundamental geometry processing problem that has been studied for decades, with many methods developed. We propose…