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Single Shot 2D Image to 3D Model

This research consists of 2 papers offering differing reconstruction approaches to obtain high accuracy inferences of 3D structures with textures from 2D single shot images.

The papers strive to challenge existing works surrounding three-dimensional reconstruction with shape and texture from only one view, which currently remain at two-dimensional shot level, obstructing obtainment of high-accuracy inference without explicit 3D supervised annotation.

On one hand, Guanyu aims to suggest an urgently-needed reconstruction approach based on meshes to enhance the 3D mesh attribute learning process, by proposing a novel and concise reconstruction model in computer vision, training shape and surface colour.

The model is realised through 2 separate pipelines by CNN:

  1. Geometric shape process learning by shape volume.
  2. The encoding-decoding system of colour, which is unified by regressed colour volume and transforming 3D to 2D flow field, respectively.

The visualisation achieved by Guanyu is a result of blending these 2 factors with appropriate weighting. To generate textured models, the one-to-one mapping fetches colours through a three-channel volume and the same space measurement as the shape network.

Point Cloud Reconstruction Amber Model

Illustration of Point Cloud Reconstruction of Amber’s Model

Meanwhile, Amber aims to create an end-to-end single view 2D to 3D reconstruction model by adopting an encoder-decoder network to use:

  1. A transformer-CNN encoder for feature extraction
  2. A decoder for shape and surface learning to generate a point cloud reconstruction
2D to 3D reconstruction model

Reconstruction Illustration on Real World Image

Consequently, the research demonstrates the proficiency of using a hybrid encoder that consists of CNN and transformer, proving successful even when trained on a small dataset.


Modelling of a modern MRF

Paloma’s paper has a dual focus on two of the challenges faced by MRFs today: The challenges associated with efficiency, waste composition variation and waste sampling methods….


Decentralised and Digitised Mini Material Recovery Facilities

This research explores the disruption of centralised waste facilities to accommodate a decentralised model, known as the mini-MRF, that is capable of extracting more value out of waste streams….


Deep Learning: Unsupervised Domain Adaptation

The goal of this WasteNet series is to engineer a Deep Learning model that will focus on UDA for the instance segmentation task. As such, there are only two other approaches that are addressing this. …