Search Results for author:
Found 30 papers, 21 papers with code
OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks
Models pretrained with our method exhibit improved BEV semantic segmentation performance, particularly in low-data scenarios.
Three Pillars improving Vision Foundation Model Distillation for Lidar
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.
BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds
We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds.
SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
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.
Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation
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)
RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving
(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.
ALSO: Automotive Lidar Self-supervision by Occupancy estimation
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.
CroCo: Cross-Modal Contrastive learning for localization of Earth Observation data
It is of interest to localize a ground-based LiDAR point cloud on remote sensing imagery.
Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data
In this context, we propose a self-supervised pre-training method for 3D perception models that is tailored to autonomous driving data.
Deep Surface Reconstruction from Point Clouds with Visibility Information
Most current neural networks for reconstructing surfaces from point clouds ignore sensor poses and only operate on raw point locations.
POCO: Point Convolution for Surface Reconstruction
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
Weakly Supervised Change Detection Using Guided Anisotropic Difusion
Large scale datasets created from crowdsourced labels or openly available data have become crucial to provide training data for large scale learning algorithms.
NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle Dropping
There has been recently a growing interest for implicit shape representations.
PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds
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.
Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds
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
Energy-based Models for Earth Observation Applications
The large amount of data, available thanks to the recent sensors, have made possible the use of deep learning for Earth Observation.
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study
The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms.
FLOT: Scene Flow on Point Clouds Guided by Optimal Transport
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.
STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation
We present a new lightweight CNN-based algorithm for multi-frame optical flow estimation.
FKAConv: Feature-Kernel Alignment for Point Cloud Convolution
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
Technical Report: Co-learning of geometry and semantics for online 3D mapping
This paper is a technical report about our submission for the ECCV 2018 3DRMS Workshop Challenge on Semantic 3D Reconstruction \cite{Tylecek2018rms}.
Surface Reconstruction from 3D Line Segments
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.
Distance transform regression for spatially-aware deep semantic segmentation
This work introduces a new semantic segmentation regularization based on the regression of a distance transform.
Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection
Large scale datasets created from user labels or openly available data have become crucial to provide training data for large scale learning algorithms.
ConvPoint: Continuous Convolutions for Point Cloud Processing
Point clouds are unstructured and unordered data, as opposed to images.
Ranked #4 on
LIDAR Semantic Segmentation
on Paris-Lille-3D
Ionospheric activity prediction using convolutional recurrent neural networks
The ionosphere electromagnetic activity is a major factor of the quality of satellite telecommunications, Global Navigation Satellite Systems (GNSS) and other vital space applications.
Fully Convolutional Siamese Networks for Change Detection
This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images.
Ranked #1 on
Change Detection
on OSCD - 13ch
Change Detection
Change detection for remote sensing images
+1
Multitask Learning for Large-scale Semantic Change Detection
In this paper we present the first large scale high resolution semantic change detection (HRSCD) dataset, which enables the usage of deep learning methods for semantic change detection.
Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate.
ShaResNet: reducing residual network parameter number by sharing weights
We show, on the one hand, that they are almost as efficient as their sequential counterparts while involving less parameters, and on the other hand that they are more efficient than a residual network with the same number of parameters.
