Jan Eric Lenssen Postdoctoral Researcher at Max Planck Institute for Informatics, Founding Engineer at Kumo.ai Short Biography PostDoc at MPI-INF under Bernt Schiele, collaborating with groups of Gerard Pons-Moll and Eddy Ilg Founding member of Kumo.ai (Stanford / TU Dortmund startup), bringing GNNs into practice PhD (Dr.-Ing.) in Computer Science from TU Dortmund University under Heinrich Müller Research internships at Facebook (Meta) Reality Labs and Nnaisense [Google Scholar] [LinkedIn] [Github] [Email]

Research Interests

Selected Publications

• Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields ECCV 2022 (Oral Presentation, Best Paper Honorable Mention Award) Garvita Tiwari, Dimitrije Antic, Jan Eric Lenssen, Nikolaos Sarafianos, Tony Tung, Gerard Pons-Moll Short Abstract: An unsigned neural distance field that models the manifold of plausible human poses in high-dimensional SO(3). Given human poses can be projected onto the manifold by SO(3) gradient descent. [Project Page] [Paper] [Code]

• TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement ECCV 2022 Keyang Zhou, Bharat Lal Bathnagar, Jan Eric Lenssen, Gerard Pons-Moll Short Abstract: A spatio-temporal representation of point-wise correspondences between a parameterized hand template mesh and an object mesh, which allows for refining captured grasping motions. [Project Page] [Paper] [Code]

• GnnAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings ICML 2021 Matthias Fey, Jan Eric Lenssen, Frank Weichert, Jure Leskovec Short Abstract: A framework to scale arbitrary message passing graph neural networks to large input graphs using historical embeddings. The scaling method provably preserves the GNN expressiveness. [Paper] [Code]

• Quaternion Equivariant Capsule Networks for 3D Point Clouds ECCV 2020 (Oral Presentation) Yongheng Zhao, Tolga Birdal, Jan Eric Lenssen, Emanuele Menegatti, Leonidas Guibas, Federico Tombari Short Abstract: A provably SO(3)-equivariant capsule network architecture to classify and canonicalize point clouds. Introduces a routing mechanism based on the iterative least-squares Weiszfeld algorithm. [Paper] [Code]

• Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction ECCV 2020 Rohan Chabra, Jan E. Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, Richard Newcombe Short Abstract: A voxel grid of implicit neural fields as local priors for representing signed distance functions. Enables detailed 3D reconstructions of full scenes while only using synthetic training data. [Paper]

• Deep Iterative Surface Normal Estimation CVPR 2020 (Oral Presentation, Best Paper Nominee) Jan Eric Lenssen, Christian Osendorfer, Jonathan Masci Short Abstract: A differentiable, iterative re-weighted least-squares (IRLS) algorithm for normal estimation on unstructured point clouds. The method combines the efficiency of traditional IRLS with a GNN data prior. [Paper] [Code]

• Deep Graph Matching Consensus ICLR 2020 Matthias Fey, Jan Eric Lenssen, Christopher Morris, Jonathan Masci, Nils Kriege Short Abstract: A two-stage siamese graph neural network for graph matching in several applications. Refines local feature matchings by optimizing neighborhood consensus in the second stage. [Paper] [Code]

• Weisfeiler and leman go neural: Higher-order graph neural networks AAAI 2019 Christopher Morris, Martin Ritzert, Matthias Fey, William Hamilton, Jan E. Lenssen, Gaurav Rattan, Martin Grohe Short Abstract: Shows equivalence in expressiveness of specific graph neural networks and the Weisfeiler-Leman graph isomorphy test. Introduces higher order GNNs with provably more expressiveness. [Paper] [Code]

• Fast Graph Representation Learning with PyTorch Geometric ICLR 2019 Workshop Matthias Fey, Jan Eric Lenssen Short Abstract: Heavily used PyTorch-based library to write and train graph neural networks for several applications. Implements efficient, differentiable and customizable message passing on GPU and CPU. [Website] [Paper] [Code]

• Group Equivariant Capsule Networks NeurIPS 2018 Jan Eric Lenssen, Matthias Fey, Pascal Libuschewski Short Abstract: A provably group equivariant capsule network for rotation invariant classification and pose estimation. Introduces an equivariant dynamic routing algorithm for images. [Paper] [Code]

• SplineCNN: Fast geometric deep learning with continuous B-spline kernels CVPR 2018 Matthias Fey*, Jan Eric Lenssen*, Frank Weichert, Heinrich Müller Short Abstract: A differentiable operator for continuous convolution on irregular-structured data. Parameterizes a continuous kernel using B-splines and provides efficient GPU implementations via message passing. [Paper] [Code]

• Real-time Low SNR Signal Processing for Nanoparticle Analysis with Deep Neural Networks BIOSIGNALS 2018 (Best Paper Award) Jan Eric Lenssen, Anas Toma, Albert Seebold, Victoria Shpacovitch, Pascal Libuschewski, Frank Weichert, Jian-Jia Chen, Roland Hergenröder Short Abstract: A multistage CNN architecture for detecting low-SNR particles in images from a surface plasmon resonance sensor. The method performs detection, classification and size estimation of nanoparticles. [Paper]