**Matheus Gadelha** ![ ](fig/me.jpg width="27%") I am currently a Research Scientist at [Adobe Research](https://research.adobe.com/). I received my PhD from [University of Massachusetts - Amherst](https://www.cics.umass.edu/) while being supervised by [Prof. Rui Wang](https://people.cs.umass.edu/~ruiwang/) and [Prof. Subhransu Maji](http://people.cs.umass.edu/~smaji/). I am interested in Computer Graphics, Vision and their intersections with Machine Learning. My work is focused on models and representations of tridimensional data for both discriminative and generative models with a particular interest in learning them directly from images. Check my Research Statement below for a more detailed overview. Prior to joining UMass, I've obtained my bachelor's and master's degrees in Computer Science from [Federal University of Rio Grande do Norte](https://sistemas.ufrn.br/portal/PT/), while working with Prof. Selan dos Santos and Prof. Bruno Motta de Carvalho. **Internships for PhD students: ** If you are interested in related areas to the ones I've mentioned above (or anything related to my previous research), don't be shy and send me an e-mail with your CV and a short description of the problems you are interested in working on. We are always looking for talented interns to join us at Adobe Research for summer internships (winter in the southern hemisphere...). **Pro-bono mentoring: ** I am more than happy to talk to you and provide any help I can regarding your career, *specially* if you belong to an [underrepresented represented group in STEM](https://en.wikipedia.org/wiki/Underrepresented_group). You can schedule a call with me [here](https://calendly.com/mgadelha/research-career-mentoring). We can discuss anything: applications to grad schools, research positions in industry, internships in industry or academia, research directions you might be interested in pursuing, and so on. [[CV](cv.pdf)] [[Research Statement](ResearchStatement.pdf)] [[Twitter](https://twitter.com/gadelha_m)] [[email](matheusabrantesgadelha@gmail.com)] [[scholar](https://scholar.google.com/citations?user=VhqmvXsAAAAJ&hl=en)] (#) Work Experience ![ ](fig/adobe_logo.png width="7%") **Adobe**. Research Scientist at San Jose, CA. _June 2021 - Now_.
---------------------------------------------------------------------------------------------------- ![ ](fig/google_logo.png width="7%") **Google (Perception)**. Research Intern in ~~Mountain View, CA~~ Amherst, MA. _Summer 2020_.
---------------------------------------------------------------------------------------------------- ![ ](fig/adobe_logo.png width="7%") **Adobe**. Research Intern at San Jose, CA. _Summer 2019_.
---------------------------------------------------------------------------------------------------- ![ ](fig/amazon_logo.png width="7%") **Amazon**. Applied Scientist Intern at Pasadena, CA. _Summer 2018_.
(#) Research ![ ](fig/prgan_journal.png width="20%") [IJCV 2020] **Inferring 3D Shapes from Image Collections using Adversarial Networks** _**Matheus Gadelha**, Aartika Rai, Rui Wang, Subhransu Maji_ [[Journal](https://link.springer.com/article/10.1007/s11263-020-01335-w)] [[Pre-print](https://arxiv.org/abs/1906.04910)] ----- ![ ](fig/acd.png width="20%") [ECCV 2020] **Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions** _**Matheus Gadelha**`*`, Aruni RoyChowdhury`*`, Gopal Sharma, Evangelos Kalogerakis, Liangliang Cao, Erik Learned-Miller, Rui Wang, Subhransu Maji_ [[Paper](https://arxiv.org/pdf/2003.13834.pdf)] [[Project Page](selfsupacd/index.html)] ----- ![ ](fig/dmp_snapshot.png width="20%") **Deep Manifold Prior** _**Matheus Gadelha**, Rui Wang, Subhransu Maji_ **Best poster honorable mention at [NECV](http://visual.cs.brown.edu/workshops/necv2019/)**. [[Paper](https://arxiv.org/pdf/2004.04242.pdf)] ----- ![ ](shapehandles/fig/snapshot.png width="20%") [CVPR 2020] **Learning Generative Models of Shape Handles** _**Matheus Gadelha**, Giorgio Gori, Duygu Ceylan, Radomir Mech, Nathan Carr, Tamy Boubekeur, Rui Wang, Subhransu Maji_ [[Project Page](shapehandles/index.html)] ----- ![ ](shaperec/fig/abstract.png width="20%") [ICCV 2019] **Shape Reconstruction with Differentiable Projections and Deep Priors ** _**Matheus Gadelha**, Rui Wang, Subhransu Maji_ [[Paper](shaperec/dsp_paper.pdf)] [[Project Page](shaperec/index.html)] ----- ![ ](fig/bayesdip.png width="20%") [CVPR 2019] **A Bayesian Perspective on the Deep Image Prior ** _Zezhou Cheng, **Matheus Gadelha**, Subhransu Maji, Daniel Sheldon_ [[Paper](https://arxiv.org/abs/1904.07457)] [[Project Page](https://people.cs.umass.edu/~zezhoucheng/gp-dip/)] ----- ![ ](https://people.cs.umass.edu/~jcsu/papers/shape_recog/advers_examples.png width="20%") [ECCV 2018 - 3DRMS Workshop] **A Deeper Look at 3D Shape Classifiers ** _Jong-Chyi Su, **Matheus Gadelha**, Rui Wang, Subhransu Maji_ [[Paper](https://arxiv.org/abs/1809.02560)] [[Project Page](https://people.cs.umass.edu/~jcsu/papers/shape_recog/)]
----- ![ ](mrt/fig/smallabs.png width="20%") [ECCV 2018] **Multiresolution Tree Networks for Point Cloud Processing ** _**Matheus Gadelha**, Rui Wang, Subhransu Maji_ [[Paper](https://arxiv.org/abs/1807.03520)] [[Project Page](mrt/index.html)]
----- ![ ](prgan/fig/overview.png width="20%") [3DV 2017] **Unsupervised 3D Shape Induction from 2D Views of Multiple Objects ** _**Matheus Gadelha**, Subhransu Maji, Rui Wang_ [[Paper](https://arxiv.org/pdf/1612.05872.pdf)] [[Project Page](prgan/index.html)]
----- ![ ](fig/sketch.png width="20%") [3DV 2017] **3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks ** _Zhaoliang Lun, **Matheus Gadelha**, Evangelos Kalogerakis, Subhransu Maji, Rui Wang_ [[ArXiv](https://arxiv.org/abs/1707.06375)] [[Project Page](https://people.cs.umass.edu/~zlun/papers/SketchModeling/)] ----- ![ ](sppc/fig/airplanes_sorting_new2.png width="20%") [BMVC 2017] **Shape Generation using Spatially Partitioned Point Clouds ** _**Matheus Gadelha**, Subhransu Maji, Rui Wang_ [[ArXiv](https://arxiv.org/abs/1707.06267)] [[Project Page](sppc/index.html)] (#) Reviewing I worked/am working as a reviewer for the following venues: CVPR 2018-2021 (**2021 outstanding reviewer**), ICCV 2019-2021, ECCV 2018-2021, ICLR 2021, TPAMI (2018, 2021), Computer and Graphics 2018, SIGGRAPH Asia 2018, Pacific Graphics 2019. (#) Open Source (##) Interactive Mesh Deformation w/ ARAP Surface Modeling ![As Rigid as Possible Interactive Demo](https://www.youtube.com/watch?v=QUtpoybhYA8) Implementation of the ARAP Surface Modeling paper. It includes a software to deform any mesh, applying the computed transformation in real time. Made using C++ with Eigen and OpenGL. [[code](https://github.com/matheusgadelha/Pintar)] (##) Rigid Body Simulation Rigid body simulator coded in C++ with Eigen library and OpenGL. The code includes a demo where you can load any model and interactively draw forces. [[code](https://github.com/matheusgadelha/Pintar)] (##) Subdivision Surface ![Smoothing bunnies w/ subdivision surfaces.](fig/bunny_gets_better.png width="25%") Code in C++ to perform subdivision of a closed triangular mesh. Made from scratch, so no libraries are required. [[code](https://github.com/matheusgadelha/SubdivisionSurfaces)] (##) Generic A* implementation and CSP solver Template-based implementation for the A* algorithm and CSP solver written in C++. It is a generic solver, where you can simply define the successor and heuristic function for each problem. Includes examples on solving knight shortest path problem and the classic TSP (w/ Minimum Spanning Tree heuristic). CSP solver includes an efficient sudoku solver example. [[A* code](https://github.com/matheusgadelha/AStar)] [[CSP code](https://github.com/matheusgadelha/CSPSolver)]