Search Results for author:
Found 20 papers, 14 papers with code
Probing the 3D Awareness of Visual Foundation Models
Given that such models can classify, delineate, and localize objects in 2D, we ask whether they also represent their 3D structure?
EgoCOL: Egocentric Camera pose estimation for Open-world 3D object Localization @Ego4D challenge 2023
We present EgoCOL, an egocentric camera pose estimation method for open-world 3D object localization.
CAD-Estate: Large-scale CAD Model Annotation in RGB Videos
We propose a method for annotating videos of complex multi-object scenes with a globally-consistent 3D representation of the objects.
Estimating Generic 3D Room Structures from 2D Annotations
Based on these 2D annotations, we automatically reconstruct 3D plane equations for the structural elements and their spatial extent in the scene, and connect adjacent elements at the appropriate contact edges.
RayTran: 3D pose estimation and shape reconstruction of multiple objects from videos with ray-traced transformers
We propose a transformer-based neural network architecture for multi-object 3D reconstruction from RGB videos.
Vid2CAD: CAD Model Alignment using Multi-View Constraints from Videos
We address the task of aligning CAD models to a video sequence of a complex scene containing multiple objects.
The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation
We present the 2019 DAVIS Challenge on Video Object Segmentation, the third edition of the DAVIS Challenge series, a public competition designed for the task of Video Object Segmentation (VOS).
Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to as "single-tasking multiple tasks".
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Iterative Deep Learning for Road Topology Extraction
This paper tackles the task of estimating the topology of road networks from aerial images.
The 2018 DAVIS Challenge on Video Object Segmentation
Motivated by the analysis of the results of the 2017 edition, the main track of the competition will be the same than in the previous edition (segmentation given the full mask of the objects in the first frame -- semi-supervised scenario).
Iterative Deep Learning for Network Topology Extraction
This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels and road networks.
Detection-aided liver lesion segmentation using deep learning
A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the according treatments.
Deep Extreme Cut: From Extreme Points to Object Segmentation
This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos.
Video Object Segmentation Without Temporal Information
Video Object Segmentation, and video processing in general, has been historically dominated by methods that rely on the temporal consistency and redundancy in consecutive video frames.
Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery
In recent works, such interventions are conducted under a stereo-microscope, and with a robot-controlled surgical tool.
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs).
One-Shot Video Object Segmentation
This paper tackles the task of semi-supervised video object segmentation, i. e., the separation of an object from the background in a video, given the mask of the first frame.
Deep Retinal Image Understanding
This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation.
Convolutional Oriented Boundaries
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs).
