49 citations · 140 across the 12 of their papers we have counts for
13 papers · 1 filter
Temporally Grounding Instructional Diagrams in Unconstrained Videos
Jiahao Zhang, Frederic Z. Zhang, Cristian Rodriguez +3
We study the challenging problem of simultaneously localizing a sequence of queries in the form of instructional diagrams in a video. This requires understanding not only the indiv…
PatchContrast: Self-Supervised Pre-training for 3D Object Detection
Oren Shrout, Ori Nizan, Yizhak Ben-Shabat +1
Accurately detecting objects in the environment is a key challenge for autonomous vehicles. However, obtaining annotated data for detection is expensive and time-consuming. We intr…
Aligning Step-by-Step Instructional Diagrams to Video Demonstrations
Jiahao Zhang, Anoop Cherian, Yanbin Liu +3
Multimodal alignment facilitates the retrieval of instances from one modality when queried using another. In this paper, we consider a novel setting where such an alignment is betw…
3DInAction: Understanding Human Actions in 3D Point Clouds
Yizhak Ben-Shabat, Oren Shrout, Stephen Gould
We propose a novel method for 3D point cloud action recognition. Understanding human actions in RGB videos has been widely studied in recent years, however, its 3D point cloud coun…
GraVoS: Voxel Selection for 3D Point-Cloud Detection
Oren Shrout, Yizhak Ben-Shabat, Ayellet Tal
3D object detection within large 3D scenes is challenging not only due to the sparsity and irregularity of 3D point clouds, but also due to both the extreme foreground-background s…
CloudWalker: Random walks for 3D point cloud shape analysis
Adi Mesika, Yizhak Ben-Shabat, Ayellet Tal
Point clouds are gaining prominence as a method for representing 3D shapes, but their irregular structure poses a challenge for deep learning methods. In this paper we propose Clou…
DiGS : Divergence guided shape implicit neural representation for unoriented point clouds
Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, Stephen Gould
Shape implicit neural representations (INRs) have recently shown to be effective in shape analysis and reconstruction tasks. Existing INRs require point coordinates to learn the im…
The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose
Yizhak Ben-Shabat, Xin Yu, Fatemeh Sadat Saleh +4
The availability of a large labeled dataset is a key requirement for applying deep learning methods to solve various computer vision tasks. In the context of understanding human ac…
DPDist : Comparing Point Clouds Using Deep Point Cloud Distance
Dahlia Urbach, Yizhak Ben-Shabat, Michael Lindenbaum
We introduce a new deep learning method for point cloud comparison. Our approach, named Deep Point Cloud Distance (DPDist), measures the distance between the points in one cloud an…
DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares
Yizhak Ben-Shabat, Stephen Gould
We propose a surface fitting method for unstructured 3D point clouds. This method, called DeepFit, incorporates a neural network to learn point-wise weights for weighted least squa…
Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks
Yizhak Ben-Shabat, Michael Lindenbaum, Anath Fischer
In this paper, we propose a normal estimation method for unstructured 3D point clouds. This method, called Nesti-Net, builds on a new local point cloud representation which consist…
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks
Yizhak Ben-Shabat, Michael Lindenbaum, Anath Fischer
The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods. The common solution of transform…
Graph Based Over-Segmentation Methods for 3D Point Clouds
Yizhak Ben-Shabat, Tamar Avraham, Michael Lindenbaum +1
Over-segmentation, or super-pixel generation, is a common preliminary stage for many computer vision applications. New acquisition technologies enable the capturing of 3D point clo…