Search Results for author:
Found 25 papers, 13 papers with code
ABC: A Big CAD Model Dataset For Geometric Deep Learning
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications.
MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers
We introduce MeshGPT, a new approach for generating triangle meshes that reflects the compactness typical of artist-created meshes, in contrast to dense triangle meshes extracted by iso-surfacing methods from neural fields.
Making DensePose fast and light
DensePose estimation task is a significant step forward for enhancing user experience computer vision applications ranging from augmented reality to cloth fitting.
Deep Vectorization of Technical Drawings
We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images.
Latent Video Transformer
After the transformation of frames into the latent space, our model predicts latent representation for the next frames in an autoregressive manner.
Ranked #13 on
Video Prediction
on Kinetics-600 12 frames, 64x64
CAD-Deform: Deformable Fitting of CAD Models to 3D Scans
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 gaming scenarios.
DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes
We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes.
Towards Part-Based Understanding of RGB-D Scans
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-grained understanding is required to enable interactions with objects and their functional understanding.
Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks
Data-driven methods such as convolutional neural networks (CNNs) are known to deliver state-of-the-art performance on image recognition tasks when the training data are abundant.
Perceptual deep depth super-resolution
RGBD images, combining high-resolution color and lower-resolution depth from various types of depth sensors, are increasingly common.
Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds
Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design.
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem
Segmentation of tumors in brain MRI images is a challenging task, where most recent methods demand large volumes of data with pixel-level annotations, which are generally costly to obtain.
fMRI: preprocessing, classification and pattern recognition
As machine learning continues to gain momentum in the neuroscience community, we witness the emergence of novel applications such as diagnostics, characterization, and treatment outcome prediction for psychiatric and neurological disorders, for instance, epilepsy and depression.
Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry
We consider a problem of diagnostic pattern recognition/classification from neuroimaging data.
Monocular 3D Object Detection via Geometric Reasoning on Keypoints
Monocular 3D object detection is well-known to be a challenging vision task due to the loss of depth information; attempts to recover depth using separate image-only approaches lead to unstable and noisy depth estimates, harming 3D detections.
Learning to Approximate Directional Fields Defined over 2D Planes
Reconstruction of directional fields is a need in many geometry processing tasks, such as image tracing, extraction of 3D geometric features, and finding principal surface directions.
Geometric Attention for Prediction of Differential Properties in 3D Point Clouds
Estimation of differential geometric quantities in discrete 3D data representations is one of the crucial steps in the geometry processing pipeline.
Unpaired Depth Super-Resolution in the Wild
We propose an unpaired learning method for depth super-resolution, which is based on a learnable degradation model, enhancement component and surface normal estimates as features to produce more accurate depth maps.
3D Parametric Wireframe Extraction Based on Distance Fields
We present a pipeline for parametric wireframe extraction from densely sampled point clouds.
Multi-sensor large-scale dataset for multi-view 3D reconstruction
We expect our dataset will be useful for evaluation and training of 3D reconstruction algorithms and for related tasks.
Scan2Part: Fine-grained and Hierarchical Part-level Understanding of Real-World 3D Scans
We propose Scan2Part, a method to segment individual parts of objects in real-world, noisy indoor RGB-D scans.
SSR-2D: Semantic 3D Scene Reconstruction from 2D Images
In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations.
PRS: Sharp Feature Priors for Resolution-Free Surface Remeshing
We define and learn a collection of surface-based fields to (1) capture sharp geometric features in the shape with an implicit vertexwise model and (2) approximate improvements in normals alignment obtained by applying edge-flips with an edgewise model.
AutoInst: Automatic Instance-Based Segmentation of LiDAR 3D Scans
To this end, we construct a learning framework consisting of two components: (1) a pseudo-annotation scheme for generating initial unsupervised pseudo-labels; and (2) a self-training algorithm for instance segmentation to fit robust, accurate instances from initial noisy proposals.
DeepMIF: Deep Monotonic Implicit Fields for Large-Scale LiDAR 3D Mapping
Recently, significant progress has been achieved in sensing real large-scale outdoor 3D environments, particularly by using modern acquisition equipment such as LiDAR sensors.
