scholarly journals Predicting 3D shapes, masks, and properties of materials inside transparent containers, using the TransProteus CGI dataset

2022 ◽  
Author(s):  
Sagi Eppel ◽  
Haoping Xu ◽  
Yi Ru Wang ◽  
Alan Aspuru-Guzik
Keyword(s):  
3D Structure ◽  
Structure And Properties ◽  
Single Image ◽  
Properties Of Materials ◽  
3D Shapes

We present TransProteus, a dataset, and methods for predicting the 3D structure and properties of materials inside transparent vessels from a single image. Manipulating materials in containers is essential in...

scholarly journals Latent 3D Volume for Joint Depth Estimation and Semantic Segmentation from a Single Image

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5765 ◽  
Author(s):  
Seiya Ito ◽  
Naoshi Kaneko ◽  
Kazuhiko Sumi
Keyword(s):  
3D Structure ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Depth Estimation ◽  
Image Features ◽  
Feature Representation ◽  
Single Image ◽  
Feature Vectors ◽  
3D Space ◽  
3D Volume

This paper proposes a novel 3D representation, namely, a latent 3D volume, for joint depth estimation and semantic segmentation. Most previous studies encoded an input scene (typically given as a 2D image) into a set of feature vectors arranged over a 2D plane. However, considering the real world is three-dimensional, this 2D arrangement reduces one dimension and may limit the capacity of feature representation. In contrast, we examine the idea of arranging the feature vectors in 3D space rather than in a 2D plane. We refer to this 3D volumetric arrangement as a latent 3D volume. We will show that the latent 3D volume is beneficial to the tasks of depth estimation and semantic segmentation because these tasks require an understanding of the 3D structure of the scene. Our network first constructs an initial 3D volume using image features and then generates latent 3D volume by passing the initial 3D volume through several 3D convolutional layers. We apply depth regression and semantic segmentation by projecting the latent 3D volume onto a 2D plane. The evaluation results show that our method outperforms previous approaches on the NYU Depth v2 dataset.

scholarly journals High Level 3D Structure Extraction from a Single Image Using a CNN-Based Approach

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 563 ◽  
Author(s):  
J. Osuna-Coutiño ◽  
Jose Martinez-Carranza
Keyword(s):  
Level Structure ◽  
3D Structure ◽  
Depth Estimation ◽  
Point Clouds ◽  
Graph Analysis ◽  
Single Image ◽  
3D Data ◽  
3D Elements ◽  
High Level ◽  
Structure Extraction

High-Level Structure (HLS) extraction in a set of images consists of recognizing 3D elements with useful information to the user or application. There are several approaches to HLS extraction. However, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds extracted from the camera images. In contrast and motivated by the extensive work developed for the problem of depth estimation in a single image, where parallax constraints are not required, in this work, we propose a novel methodology towards HLS extraction from a single image with promising results. For that, our method has four steps. First, we use a CNN to predict the depth for a single image. Second, we propose a region-wise analysis to refine depth estimates. Third, we introduce a graph analysis to segment the depth in semantic orientations aiming at identifying potential HLS. Finally, the depth sections are provided to a new CNN architecture that predicts HLS in the shape of cubes and rectangular parallelepipeds.

Effect of high pressure on the structure and properties of materials based on boron carbide

1996 ◽  
Vol 34 (9-10) ◽  
pp. 491-495 ◽  
Author(s):  
V. R. Maslennikova ◽  
A. A. Belkina ◽  
A. D. Panasyuk ◽  
L. I. Struk ◽  
V. P. Smirnov
Keyword(s):  
High Pressure ◽  
Boron Carbide ◽  
Structure And Properties ◽  
Properties Of Materials

Engineering Structure and Properties of Materials Used as a Matrix in Diamond Impregnated Tools / Kształtowanie Struktury I Własnosci Materiałów Stosowanych Jako Osnowa W Narzedziach Metaliczno-Diamentowych

2013 ◽  
Vol 58 (1) ◽  
pp. 5-8 ◽  
Author(s):  
J. Borowiecka-Jamrozek
Keyword(s):  
Mechanical Properties ◽  
Particle Size ◽  
Structure And Properties ◽  
Hot Press ◽  
Properties Of Materials ◽  
The Mean ◽  
Materials Used ◽  
Hot Press Process ◽  
Scanning Electron ◽  
And Tungsten

The paper presents mechanical properties of materials used as matrices in diamond impregnated tools. Several powder metallurgy materials were manufactured by the hot press process from various combinations of cobalt (Co SMS, Co Extrafine, Co 400mesh), carbonyl iron (Fe CN) and tungsten (WP30) powders. After consolidation the specimens were tested for density, hardness and tensile properties. The fracture surfaces and materials’ microstructure were observed using the Jeol JSM- 5400 scanning electron microscope and the Leica DM4000 light microscope. The main objective of the work was to determine the effects of the mean particle size of cobalt as well as additions of iron and tungsten on properties of the as-consolidated material.

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