We present an innovative approach to 3D Human Pose Estimation (3D-HPE) b...
We propose SeedAL, a method to seed active learning for efficient annota...
Learning models on one labeled dataset that generalize well on another d...
Casting semantic segmentation of outdoor LiDAR point clouds as a 2D prob...
Semantic segmentation of point clouds in autonomous driving datasets req...
We propose a new self-supervised method for pre-training the backbone of...
We propose a simple, yet powerful approach for unsupervised object
segme...
Predictive performance of machine learning models trained with empirical...
Segmenting or detecting objects in sparse Lidar point clouds are two
imp...
Most current neural networks for reconstructing surfaces from point clou...
Implicit neural networks have been successfully used for surface
reconst...
There has been recently a growing interest for implicit shape
representa...
Rigid registration of point clouds with partial overlaps is a longstandi...
Localizing objects in image collections without supervision can help to ...
While there has been a number of studies on Zero-Shot Learning (ZSL) for...
We introduce a novel learning-based, visibility-aware, surface reconstru...
Motivated by the need of estimating the pose (viewpoint) of arbitrary ob...
Detecting objects and estimating their viewpoint in images are key tasks...
We formalize concepts around geometric occlusion in 2D images (i.e., ign...
We propose and study a method called FLOT that estimates scene flow on p...
Batch Normalization (BN) is a prominent deep learning technique. In spit...
Recent state-of-the-art methods for point cloud semantic segmentation ar...
In man-made environments such as indoor scenes, when point-based 3D
reco...
Most deep pose estimation methods need to be trained for specific object...
Localizing an object accurately with respect to a robot is a key step fo...
Usual Structure-from-Motion (SfM) techniques require at least trifocal
o...
Convolutional Neural Networks (CNNs) were recently shown to provide
stat...
This paper introduces a fast and efficient segmentation technique for 2D...
In this paper we study the application of convolutional neural networks ...