mathieuaubry

mathieuaubry's Stars

• drprojects/superpoint_transformer

  Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering"

  Language:Python5631114573

• cvlab-stonybrook/DewarpNet

  Code for the paper "DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks" (ICCV '19)

  Language:Python492165995

• XiSHEN0220/RANSAC-Flow

  (ECCV 2020) RANSAC-Flow: generic two-stage image alignment

  Language:Jupyter Notebook433182376

• ylabbe/cosypose

  Code for "CosyPose: Consistent multi-view multi-object 6D pose estimation", ECCV 2020.

  Language:Python301157689

• fdarmon/NeuralWarp

  Code release of paper Improving neural implicit surfaces geometry with patch warping

  Language:Python23281814

• YoungXIAO13/FewShotDetection

  (ECCV 2020) PyTorch implementation of paper "Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild"

  Language:Python21184633

• megapose6d/megapose6d

  Code for "MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare", CoRL 2022.

  Language:Jupyter Notebook204106728

• monniert/differentiable-blocksworld

  [NeurIPS 2023] Code for "Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives"

  Language:Python19281010

• monniert/unicorn

  (ECCV 2022) Code for Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency

  Language:Python1627815

• fwilliams/deep-geometric-prior

  The reference implementaiton for the paper "Deep Geometric Prior for Surface Reconstruction"

  Language:Python12571016

• monniert/docExtractor

  (ICFHR 2020 oral) Code for "docExtractor: An off-the-shelf historical document element extraction" paper

  Language:Python8572210

• monniert/dti-clustering

  (NeurIPS 2020 oral) Code for "Deep Transformation-Invariant Clustering" paper

  Language:Python743313

• ylabbe/robopose

  Code for "Single-view robot pose and joint angle estimation via render & compare", CVPR 2021 (Oral).

  Language:Python657411

• XiSHEN0220/WatermarkReco

  Pytorch implementation of the paper "Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach"

  Language:Python59519

• romainloiseau/LearnableEarthParser

  Official Pytorch implementation of the "Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans" paper

  Language:Python56524

• YoungXIAO13/FewShotViewpoint

  (ECCV 2020) PyTorch implementation of paper "Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild"

  Language:Python54555

• XiSHEN0220/SegSwap

  (CVPRW 2022) Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

  Language:Python50504

• ylabbe/rearrangement-planning

  Vision code for "Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning", RA-L 2020.

  Language:Jupyter Notebook46802

• monniert/dti-sprites

  (ICCV 2021) Code for "Unsupervised Layered Image Decomposition into Object Prototypes" paper

  Language:Python45237

• tim885/P2ORM

  (ECCV 2020 Spotlight) Pixel-Pair Occlusion Relationship Map (P2ORM): Formulation, Inference & Application

  Language:Python44422

• ElliotVincent/AgriITSC

  Prototype-based method for agricultural image time series classification.

  Language:Python35104

• ElliotVincent/SitsSCD

  Implementation of the multi-temporal UTAE for the task of satellite image time series semantic change detection (SITS-SCD)

  Language:Python32210

• romainloiseau/deep-linear-shapes

  PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper.

  Language:Jupyter Notebook31102

• vloison/Handwritten_Text_Recognition

  Language:Python31445

• ysig/learnable-typewriter

  The Learnable Typewriter: A Generative Approach to Text Line Analysis

  Language:Python26201

• facebookresearch/fewshotDatasetDesign

  The paper studies the problem of learning to recognize a new class of objects from a very small number of labeled images. This is called few-shot learning. Previous work in the literature focused on designing new algorithms that allow to learn to generalize to new unseen classes.In this work, we consider the impact of the dataset that we train on, and experiment with some dataset manipulations to see which trade-offs are important in the design of a dataset aimed at few-shot learning.

  Language:Jupyter Notebook25726

• romainloiseau/EarthParserDataset

  Toolbox for the Earth Parser Dataset, a dataset presented in the "Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans" paper

  Language:Python25301

• Rykoua/ImageCollation

  Language:Python14303

• vayvi/HDV

  Historical Diagram Vectorization

  Language:Jupyter Notebook11200

• ymontmarin/adamsrt

  Spherical Perspective on Learning with Batch Norm - New optimization schemes

  Language:Python9203