Co-Locating Style-Defining Elements on 3D Shapes

Ruizhen Hu; Wenchao Li; Oliver Van Kaick; Hui Huang; Melinos Averkiou; Daniel Cohen-Or; Hao Zhang

doi:10.1145/3092817

Analysis of students’ problem solving skills of geometry 3D shapes mathematical problems through Krulik and Rudnick steps

10.1063/5.0043376 ◽

2021 ◽

Author(s):

Nudiya Ilma Atsara ◽

Sukoriyanto ◽

Makbul Muksar

Keyword(s):

Problem Solving ◽

Problem Solving Skills ◽

Mathematical Problems ◽

3D Shapes

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Learning the Incremental Warp for 3D Vehicle Tracking in LiDAR Point Clouds

Remote Sensing ◽

10.3390/rs13142770 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2770

Author(s):

Shengjing Tian ◽

Xiuping Liu ◽

Meng Liu ◽

Yuhao Bian ◽

Junbin Gao ◽

...

Keyword(s):

Point Cloud ◽

Point Clouds ◽

Vehicle Tracking ◽

Complex Nature ◽

Global Feature ◽

Tracking Accuracy ◽

State Estimator ◽

Latent Space ◽

3D Shapes ◽

Cloud Tracking

Object tracking from LiDAR point clouds, which are always incomplete, sparse, and unstructured, plays a crucial role in urban navigation. Some existing methods utilize a learned similarity network for locating the target, immensely limiting the advancements in tracking accuracy. In this study, we leveraged a powerful target discriminator and an accurate state estimator to robustly track target objects in challenging point cloud scenarios. Considering the complex nature of estimating the state, we extended the traditional Lucas and Kanade (LK) algorithm to 3D point cloud tracking. Specifically, we propose a state estimation subnetwork that aims to learn the incremental warp for updating the coarse target state. Moreover, to obtain a coarse state, we present a simple yet efficient discrimination subnetwork. It can project 3D shapes into a more discriminatory latent space by integrating the global feature into each point-wise feature. Experiments on KITTI and PandaSet datasets showed that compared with the most advanced of other methods, our proposed method can achieve significant improvements—in particular, up to 13.68% on KITTI.

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Tuning Three-dimensional (3D) Shapes of Polymeric Microparticles by Geometry-driven Control of Mold Swelling and Capillarity in Micromolds

Journal of Colloid and Interface Science ◽

10.1016/j.jcis.2021.05.045 ◽

2021 ◽

Author(s):

Reya Ganguly ◽

Yoon Choi ◽

Chang-Soo Lee ◽

Chang-Hyung Choi

Keyword(s):

Three Dimensional ◽

Polymeric Microparticles ◽

3D Shapes

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Bacterial cellulose spheroids as building blocks for 3D and patterned living materials and for regeneration

Nature Communications ◽

10.1038/s41467-021-25350-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Joaquin Caro-Astorga ◽

Kenneth T. Walker ◽

Natalia Herrera ◽

Koon-Yang Lee ◽

Tom Ellis

Keyword(s):

Bacterial Cellulose ◽

Building Blocks ◽

Genetically Engineered ◽

High Yield ◽

Synthetic Materials ◽

Chemical Inputs ◽

First Time ◽

3D Shapes ◽

Promising Avenue ◽

Living Materials

AbstractEngineered living materials (ELMs) based on bacterial cellulose (BC) offer a promising avenue for cheap-to-produce materials that can be programmed with genetically encoded functionalities. Here we explore how ELMs can be fabricated in a modular fashion from millimetre-scale biofilm spheroids grown from shaking cultures of Komagataeibacter rhaeticus. Here we define a reproducible protocol to produce BC spheroids with the high yield bacterial cellulose producer K. rhaeticus and demonstrate for the first time their potential for their use as building blocks to grow ELMs in 3D shapes. Using genetically engineered K. rhaeticus, we produce functionalized BC spheroids and use these to make and grow patterned BC-based ELMs that signal within a material and can sense and report on chemical inputs. We also investigate the use of BC spheroids as a method to regenerate damaged BC materials and as a way to fuse together smaller material sections of cellulose and synthetic materials into a larger piece. This work improves our understanding of BC spheroid formation and showcases their great potential for fabricating, patterning and repairing ELMs based on the promising biomaterial of bacterial cellulose.

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