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Internship: Domain Adaptation for Segmentation of 3D Scans

   Adversarial Domain Adaptation for Sensor-Robust Panoptic Segmentation of 3D Scans

 

Collaboration with Laboratory: STRUDEL team, LaSTIG laboratory (IGN/Univ. Gustave Eiffel)

Location: IGN: Saint Mandé, France (3 min outside Paris), SAMP: Station F, Paris

Advisors: Loic Landrieu, PhD and Shivani Shah, PhD

Remuneration: 1200 euros gross / month

Starting Date: May 2021, 5 months duration

Key words: Domain Adaptation, 3D Data, Panotic Segmentation, Deep Learning

Development Environment: Linux, Python, PyTorch.

 

Internship Context

This internship is proposed in collaboration with the STRUDEL laboratory at IGN. STRUDEL Team is a machine-learning research team with IGN, the French Mapping Agency. It focuses on solving large-scale computer vision and remote sensing challenges by developing state-of-the-art methods. In particular, it focuses on scalable 3D deep learning  and open-source frameworks.

 

Objectives

Bolstered by the rapid progress of 3D sensor technology, private and public actors have seen a stark increase in both the quantity and quality of available 3D data. Alongside this accessibility, recent methodological advancement in terms of deep learning applied to 3D data have considerably improved the capacity for automated analysis of 3D scans. However, training high performance deep learning methods requires large quantities of annotated data. Furthermore, this approach tends to be very sensitive to the data distribution used in the training phase.

Companies such as SAMP have access to a large amount of data to train their models. However, the scans come from many different sites that may differ in their nature (Nuclear Plants, Oil and Gas plants, Chemical Plants, Manufacturing Plants) as well as the characteristics of the sensors. This makes it hard to leverage the quantity of available data to train models across several datasets.

The objective of this internship is to implement an approach allowing to train a network to analyse 3D data of different industrial sites and acquired with a variety of sensors. To this end, an existing panoptic segmentation network will be modified in order to handle the difference in data distribution. The intern will investigate adversarial domain adaptation techniques such as DAN : by making the learned features indistinguishable across all sites, a single model can leverage the entirety of SAMP database.

The tasks of this internship are as follows:

  1. Understand and familiarize with the models developed within SAMP for panoptic semantic segmentation, as well as the characteristics of the available datasets.
  2. Implement a domain adaptation training routine across different dataset in order to make features Site-independent. 
  3. Train a model across all available datasets.
  4. Validate the approach on available public datasets.
  5. Validate approach on SAMP’s datasets.

Provided satisfying results, this work will lead to the writing of a conference paper with the student.

Required Profile

  • Student in Master 2 in computer science, applied mathematics or other relevant courses
  • Familiarity with machine learning and computer vision concepts
  • Experienced with Python and familiar with PyTorch
  • Curiosity, rigor
  • (Optional) Experienced with 3D neural networks and versioning interfaces (github)
  • (Optional) Good level of written English.

 

The personal data collected by means of the forms on this website will be kept and processed by Samp, for reasons of recruitment purposes only. Such data will be kept for a duration of maximum 24 months and processed in accordance with the provisions of the General Data Protection Regulation (GDPR) and any compulsory provisions of applicable data privacy laws. The data will be kept and processed on France. The www.samp.ai website users can exercise their data subject rights by contacting the privacy officer of Samp at privacy@samp.ai
SAMP SAS
Station F
5 Parvis Alan Turing
75013 Paris
France

ABOUT SAMP

Samp is a deep tech startup that helps large industrial facilities successfully go through constant modernization required to meet the fundamental challenges of safety and sustainability.
At Samp, we believe that our ability as a society to collectively transition to a sustainable world first and foremost depends upon how we will manage our large industrial facilities.
We provide software solutions that capture and exposes a reliable 3D model of the facilities to all players, a “Digital Twin”.
We are backed by one of Europe’s leading VC firm – Entrepreneur First – and are based in Station F.

Laurent, our CEO, has a PhD and 15 years of experience in the Energy industry. He has held important positions in companies like ENGIE and Wood Plc. Most recently he was
Technical Director at Dassault Systèmes, in charge of the solutions for the Energy & Materials division.

Shivani, our CTO, has a PhD in Machine Learning from CEA Saclay.
Our team is mentored by a team of advisors composed of senior academic and business experts having occupied executive positions in large energy companies.

SAMP SAS

Station F
5 Parvis Alan Turing
75013 Paris
France