2 code implementations • 29 Sep 2021 • Oriane Siméoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet, Jean Ponce
We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points.
Ranked #4 on Weakly-Supervised Object Localization on CUB-200-2011 (Top-1 Localization Accuracy metric)
2 code implementations • ECCV 2020 • Yang Xiao, Vincent Lepetit, Renaud Marlet
In this paper, we tackle the problems of few-shot object detection and few-shot viewpoint estimation.
Ranked #14 on Few-Shot Object Detection on MS-COCO (30-shot)
1 code implementation • CVPR 2022 • Alexandre Boulch, Renaud Marlet
To overcome this limitation, a few approaches infer latent vectors on a coarse regular 3D grid or on 3D patches, and interpolate them to answer occupancy queries.
Ranked #2 on 3D Reconstruction on ShapeNet
2 code implementations • 12 Jun 2019 • Yang Xiao, Xuchong Qiu, Pierre-Alain Langlois, Mathieu Aubry, Renaud Marlet
Most deep pose estimation methods need to be trained for specific object instances or categories.
1 code implementation • CVPR 2022 • Corentin Sautier, Gilles Puy, Spyros Gidaris, Alexandre Boulch, Andrei Bursuc, Renaud Marlet
In this context, we propose a self-supervised pre-training method for 3D perception models that is tailored to autonomous driving data.
1 code implementation • CVPR 2023 • Alexandre Boulch, Corentin Sautier, Björn Michele, Gilles Puy, Renaud Marlet
The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled, and to use the underlying latent vectors as input to the perception head.
1 code implementation • 23 Mar 2017 • Yohann Salaun, Renaud Marlet, Pascal Monasse
Usual Structure-from-Motion (SfM) techniques require at least trifocal overlaps to calibrate cameras and reconstruct a scene.
2 code implementations • 1 Nov 2019 • Pierre-Alain Langlois, Alexandre Boulch, Renaud Marlet
In man-made environments such as indoor scenes, when point-based 3D reconstruction fails due to the lack of texture, lines can still be detected and used to support surfaces.
1 code implementation • ECCV 2020 • Gilles Puy, Alexandre Boulch, Renaud Marlet
Our main finding is that FLOT can perform as well as the best existing methods on synthetic and real-world datasets while requiring much less parameters and without using multiscale analysis.
1 code implementation • CVPR 2023 • Angelika Ando, Spyros Gidaris, Andrei Bursuc, Gilles Puy, Alexandre Boulch, Renaud Marlet
(c) We refine pixel-wise predictions with a convolutional decoder and a skip connection from the convolutional stem to combine low-level but fine-grained features of the the convolutional stem with the high-level but coarse predictions of the ViT encoder.
1 code implementation • 26 Oct 2023 • Corentin Sautier, Gilles Puy, Alexandre Boulch, Renaud Marlet, Vincent Lepetit
We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds.
1 code implementation • 23 Jul 2020 • Xuchong Qiu, Yang Xiao, Chaohui Wang, Renaud Marlet
The former provides a way to generate large-scale accurate occlusion datasets while, based on the latter, we propose a novel method for task-independent pixel-level occlusion relationship estimation from single images.
1 code implementation • 12 May 2021 • Yang Xiao, Yuming Du, Renaud Marlet
We experimented on Pascal3D+, ObjectNet3D and Pix3D in a cross-dataset fashion, with both seen and unseen classes.
1 code implementation • 9 Apr 2020 • Alexandre Boulch, Gilles Puy, Renaud Marlet
Recent state-of-the-art methods for point cloud processing are based on the notion of point convolution, for which several approaches have been proposed.
Ranked #1 on LIDAR Semantic Segmentation on Paris-Lille-3D
1 code implementation • 6 Apr 2023 • Bjoern Michele, Alexandre Boulch, Gilles Puy, Tuan-Hung Vu, Renaud Marlet, Nicolas Courty
Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains.
1 code implementation • 13 Jul 2021 • Raphael Sulzer, Loic Landrieu, Renaud Marlet, Bruno Vallet
We introduce a novel learning-based, visibility-aware, surface reconstruction method for large-scale, defect-laden point clouds.
1 code implementation • 3 Feb 2022 • Raphael Sulzer, Loic Landrieu, Alexandre Boulch, Renaud Marlet, Bruno Vallet
Most current neural networks for reconstructing surfaces from point clouds ignore sensor poses and only operate on raw point locations.
1 code implementation • ICCV 2023 • Gilles Puy, Alexandre Boulch, Renaud Marlet
Semantic segmentation of point clouds in autonomous driving datasets requires techniques that can process large numbers of points efficiently.
Ranked #4 on LIDAR Semantic Segmentation on nuScenes (val mIoU metric)
1 code implementation • 13 Aug 2021 • Björn Michele, Alexandre Boulch, Gilles Puy, Maxime Bucher, Renaud Marlet
While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D images, its application to 3D data is still recent and scarce, with just a few methods limited to classification.
Ranked #1 on Generalized Zero-Shot Learning on ScanNet
1 code implementation • 30 Nov 2021 • Alexandre Boulch, Pierre-Alain Langlois, Gilles Puy, Renaud Marlet
There has been recently a growing interest for implicit shape representations.
1 code implementation • ICCV 2021 • Anh-Quan Cao, Gilles Puy, Alexandre Boulch, Renaud Marlet
Rigid registration of point clouds with partial overlaps is a longstanding problem usually solved in two steps: (a) finding correspondences between the point clouds; (b) filtering these correspondences to keep only the most reliable ones to estimate the transformation.
1 code implementation • 31 Jan 2023 • Raphael Sulzer, Renaud Marlet, Bruno Vallet, Loic Landrieu
We present a comprehensive survey and benchmark of both traditional and learning-based methods for surface reconstruction from point clouds.
1 code implementation • ICCV 2023 • Nermin Samet, Oriane Siméoni, Gilles Puy, Georgy Ponimatkin, Renaud Marlet, Vincent Lepetit
Assuming that images of the point clouds are available, which is common, our method relies on powerful unsupervised image features to measure the diversity of the point clouds.
1 code implementation • 19 Sep 2022 • Georgy Ponimatkin, Nermin Samet, Yang Xiao, Yuming Du, Renaud Marlet, Vincent Lepetit
We propose a simple, yet powerful approach for unsupervised object segmentation in videos.
Ranked #1 on Unsupervised Video Object Segmentation on SegTrack v2 (Jaccard (Mean) metric)
1 code implementation • 26 Oct 2023 • Gilles Puy, Spyros Gidaris, Alexandre Boulch, Oriane Siméoni, Corentin Sautier, Patrick Pérez, Andrei Bursuc, Renaud Marlet
In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality.
1 code implementation • 23 Jun 2020 • Simon Roburin, Yann de Mont-Marin, Andrei Bursuc, Renaud Marlet, Patrick Pérez, Mathieu Aubry
Normalization Layers (NLs) are widely used in modern deep-learning architectures.
no code implementations • 13 Sep 2016 • Francisco Massa, Renaud Marlet, Mathieu Aubry
Convolutional Neural Networks (CNNs) were recently shown to provide state-of-the-art results for object category viewpoint estimation.
no code implementations • 21 Jun 2016 • Raghudeep Gadde, Varun Jampani, Renaud Marlet, Peter V. Gehler
This paper introduces a fast and efficient segmentation technique for 2D images and 3D point clouds of building facades.
no code implementations • 22 Dec 2014 • Francisco Massa, Mathieu Aubry, Renaud Marlet
In this paper we study the application of convolutional neural networks for jointly detecting objects depicted in still images and estimating their 3D pose.
no code implementations • CVPR 2015 • Mateusz Kozinski, Raghudeep Gadde, Sergey Zagoruyko, Guillaume Obozinski, Renaud Marlet
We present a new shape prior formalism for segmentation of rectified facade images.
no code implementations • 7 Feb 2019 • Vianney Loing, Renaud Marlet, Mathieu Aubry
Localizing an object accurately with respect to a robot is a key step for autonomous robotic manipulation.
no code implementations • ECCV 2020 • Xuchong Qiu, Yang Xiao, Chaohui Wang, Renaud Marlet
Inference & Application","We formalize concepts around geometric occlusion in 2D images (i. e., ignoring semantics), and propose a novel unified formulation of both occlusion boundaries and occlusion orientations via a pixel-pair occlusion relation.
no code implementations • 26 Aug 2022 • Simon Roburin, Charles Corbière, Gilles Puy, Nicolas Thome, Matthieu Aubry, Renaud Marlet, Patrick Pérez
Predictive performance of machine learning models trained with empirical risk minimization (ERM) can degrade considerably under distribution shifts.
no code implementations • 4 Sep 2023 • Cédric Rommel, Eduardo Valle, Mickaël Chen, Souhaiel Khalfaoui, Renaud Marlet, Matthieu Cord, Patrick Pérez
We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively unexplored in 3D-HPE.
no code implementations • 11 Dec 2023 • Cédric Rommel, Victor Letzelter, Nermin Samet, Renaud Marlet, Matthieu Cord, Patrick Pérez, Eduardo Valle
Monocular 3D human pose estimation (3D-HPE) is an inherently ambiguous task, as a 2D pose in an image might originate from different possible 3D poses.
no code implementations • 22 Apr 2024 • Sophia Sirko-Galouchenko, Alexandre Boulch, Spyros Gidaris, Andrei Bursuc, Antonin Vobecky, Patrick Pérez, Renaud Marlet
Models pretrained with our method exhibit improved BEV semantic segmentation performance, particularly in low-data scenarios.