Leonid Keselman

I am a PhD student at the Robotics Institute, part of the School of Computer Science at Carnegie Mellon University, where I work on 3D computer vision. My PhD advisor is Martial Hebert.

From 2011 to 2017, I worked at Intel, as part of Intel RealSense. I primarily designed computer vision algorithms for efficient hardware ASICs, including the Intel RealSense R200 and D400 RGB-D sensors. Additionally, I worked on software APIs, active illumination systems, human-computer interaction devices, and helped develop demos for trade shows, including CES 2012-2016.

I have an MS in Computer Science (AI focus) from Stanford University, where I was a research assistant for Silvio Savarese and a teaching assistant for Fei-Fei Li (CS131 & CS231N). I have a BS in EECS from UC Berkeley, where I worked in Kris Pister's lab.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

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Research

I'm interested in computer vision, machine learning, optimization, graphics and robotics.

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Venue Analytics: A Simple Alternative to Citation-Based Metrics


Leonid Keselman
ACM/IEEE Joint Conference on Digital Libraries, 2019
arxiv / code / slides /

A bibliometric/scientometric project. Our main two results are a method of assigning value to all venues in computer science and a method to organize them into distinct subfields, all without using citation data. The resulting venue scores can be used to rank universities’ by scholarly output, and produce a responsive author-level metric.

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Direct Fitting of Gaussian Mixture Models


Leonid Keselman, Martial Hebert
Computer and Robot Vision Conference, 2019
arxiv / code / slides /

A formulation for fitting Gaussian Mixture Models (GMMs) directly to geometric objects, such as a triangular mesh. This method produces more robust results and produces an improvement in 3D registration for both meshes and RGB-D frames.

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Intel RealSense Stereoscopic Depth Cameras


Leonid Keselman, John Iselin Woodfill, Anders Grunnet-Jepsen, Achintya Bhowmik
CVPR Workshops (Computational Cameras and Displays), 2017
arxiv /

Technical and design details of the Intel RealSense R200 and D400 series

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Rigid-body Dynamics for Articulated Mesh Tracking


Leonid Keselman, Sterling Orsten, Stan Melax
CVPR Workshops (HANDS), 2015
slides /

An invited talk for the HANDS 2015 workshop at CVPR 2015. This includes further details about the efficiency of our rigid-body solver, our machine-learning tools, and some details about our data annotation process.

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Dynamics Based Hand Tracking


Stan Melax, Leonid Keselman, Sterling Orsten
Graphics Interfaces, 2013
arxiv / code /

Using a physics engine (e.g. a dynamics solver) to track 3D articulated objects in real-time.

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Dynamics Based Hand Tracking


Stan Melax, Leonid Keselman, Sterling Orsten
ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 2013
poster /

A tracking algorithm that was real-time on a consumer laptop. Won Best Poster Award.




Intel Projects

Besides my work on the RealSense depth sensors and the publications above, a sampling of my publicly disclosed work

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Intel RealSense 400


Intel
2016-08-15

My responsibilities included system performance, components of the stereo algorithm on the imaging ASIC, and contributions to the design of laser projector pattern.

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Compact VCSEL Projector


Intel
2016-06-27
patent / patent #2 / patent #3 /

A low-cost dense, configurable projector system for RGB-D depth sensors.

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Depth Image Enhancement


Intel
2015-08-06
patent /

Algorithms to filter, enhance and clean-up RGB-D data streams.

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Real-time Box Measurement


Intel
2015-04-08
video / video #2 /

Using a single depth sensor, real-time detection of cuboids, accurate estimation of their dimensions, and even some bin-packing.

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DashPoint: A low-cost, low-power human interface device


Intel
2013-06-07
patent / patent #2 /

Finger tracking on a microcontroller, with optics tricks and some HCI ideas

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Stereoscopic depth reconstruction with probabilistic pixel correspondence search


Intel
2012-07-24
patent /

A fast method for performing stereo depth maps.




Other Projects

These include coursework, side projects and unpublished research work.

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Dice Stacking: A Dynamic Manipulation Task


CMU 16-741 Mechanics of Manipulation
2018-12-05
paper / video / code /

With Hunter Goforth, we designed a manipulation task and solved it with imitation learning.

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Introspective Neural Networks


CMU 16-824: Visual Learning and Recognition
2018-05-15
paper /

Using pre-trained neural networks to improve fine grained recognition via style transfer.

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Stochastic Sampling of Parametric Policies


CMU 16-745: Dynamic Optimization
2018-05-05
paper /

Using a very simple algorithm to solve some very simple environments

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Optimizing for Physical Simulation


CMU 16-745: Dynamic Optimization
2018-03-22
code /

With Chris Atkeson and Alex Spitzer. Using optimizers to match an observed trajectory.

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A Maze Bot


Stanford CS225A: Experimental Robotics
2017-06-12
paper / video / video #2 /

Making a 6-DoF PUMA arm solve a maze with real-time vision and tracking.

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Learning Implicit Communication Strategies


Stanford CS234: Deep Reinforcement Learning
2017-06-10

Work with Aaron Goodman on used reinforcement learning to discover implicit collusion strategies in the context of an iterated prisoner’s dilemma.

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Computational models for text summarization


Stanford CS224N: Natural Language Processing
2017-03-18
paper / video / code / poster /

Work with Ludwig Schubert on simplified encoders stages for text summarization.

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Superresolution Micrscopy


Stanford CS371: Computational Biology in Four Dimensions
2017-03-16
code / slides /

An implementation of Faster STORM using compressed sensing.

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Automatically building Restaurant Ontologies


Stanford CS270: Modeling Biomedical Systems
2017-03-15
paper / poster /

Using the Yelp dataset of reviews to model the semantics and relationships between cuisines, businesses and other properties useful for restaurant recommendations.

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Beyond Correlation Networks for the Financial Market


Stanford CS224W: Social and Information Network Analysis
2016-12-07
paper /

Using graph models, we track the development of financial networks over the 20th century.

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Gradient-learned Models for Stereo Matching


Stanford CS231A: Computer Vision, From 3D Reconstruction to Recognition
2016-06-07
paper / code /

Some re-implementations of standard stereo correspondence algorithms, along with experiments using classification for stereo matching.

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Multimodal Natural Language Inference


Stanford CS224U: Natural Language Understanding
2016-06-06
paper / video /

We explored how natural language inference tasks can be augmented with visual data.

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CNNs for 3D Model Classification


Stanford CS231n: Convolutional Neural Networks for Visual Recognition
2016-03-08
paper / poster /

3D shape classification by learning an embedding function into a 2D image and using a pre-trained ImageNet network. At the time, got state-of-the-art results for single-view classification on ShapeNet40.

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Wide-angle Stereo Lenses


Stanford CS448I: Computational Imaging and Display
2016-03-07
paper / poster /

We introduce various projection functions in the analysis of stereoscopic depth sensors.

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Doctor Bayes


Stanford CS221: Artificial Intelligence
2015-12-12
website / paper / code / poster /

Detecting disease from a short description of symptoms. In some small testing, obtained nearly 90% top 5 accuracy and about 60% top 1 accuracy

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Level-set based tracking and segmentation


Stanford CS279: Structure and Organization of Biomolecules and Cells
2015-12-04
paper / code /

We implemented a detection and deformable tracking pipeline for red blood cells.

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Dequantization of Depth Data


Other
2015-04-22
code /

An O(1) time algorithm for producing smooth normals for quantized data, such as the Kinect.

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Golf swing monitoring


Other
2011-07-21

Work with Ankur Mehta, built a demonstration platform that used wireless low-weight, low-cost sensor platforms to monitor a golf swing.

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Project Tetra: Collaborative robot state estimation


UC Berkeley EE149: Embedded Systems
2011-07-21

With Humphrey Hu, Ryan Julian, and Eric Yuan, a project to show the efficacy of multiple-robot collaborative state estimation. Using Wiimote cameras, mobile robot platforms, and real-time wireless communication.

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GINA: Low power design


UC Berkeley
2010-08-21

For testing and validating the functionality of the GINA (Guidance and Inertial NAvigation) mote, a 1.6 gram sensor platform.

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GINA: Wireless sensor platform


UC Berkeley
2010-06-22

I helped Anita Flynn and Thomas Watteyne build these small sensors and wrote firmware.


Design and source code from Jon Barron's website