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 /
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Research

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

	Flexible Techniques for Differentiable Rendering with 3D Gaussians Leonid Keselman, Martial Hebert arXiv, 2023 arxiv / code / website / We show how shape reconstruction with 3D Gaussians can be expanded to include differentiable optical flow, colored mesh exports and more.
	Optimizing Algorithms From Pairwise User Preferences Leonid Keselman, Katherine Shih, Martial Hebert, Aaron Steinfeld International Conference on Intelligent Robots and Systems, 2023 arxiv / code / website / We show how to perform efficient black-box optimization of algorithm configuration from user preferences. Results include Intel RealSense stereo cameras and a robot social navigation policy.
	Discovering Multiple Algorithm Configurations Leonid Keselman, Martial Hebert International Conference on Robotics and Automation, 2023 arxiv / code / website / youtube / We show the benefits of discovering an ensemble of configurations for a given algorithm during the course of optimization. Results on stereo, planning and visual odometry.
	Fuzzy Metaballs: Approximate Differentiable Rendering with Algebraic Surfaces Leonid Keselman, Martial Hebert European Conference on Computer Vision (Oral), 2022 arxiv / code / website / youtube / An approximate differentiable renderer for a compact, interpretable representation, which we call Fuzzy Metaballs. Our approximate renderer focuses on rendering shapes via depth maps and silhouettes. It sacrifices fidelity for utility, producing fast runtimes and high-quality gradient information that can be used to solve vision tasks.
	Venue Analytics: A Simple Alternative to Citation-Based Metrics Leonid Keselman ACM/IEEE Joint Conference on Digital Libraries, 2019 arxiv / code / slides / website / 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.
	Direct Fitting of Gaussian Mixture Models Leonid Keselman, Martial Hebert Computer and Robot Vision Conference, 2019 arxiv / code / slides / website / 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.
	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
	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.
	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.
	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

	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.
	Compact VCSEL Projector Intel 2016-06-27 patent / patent #2 / patent #3 / A low-cost dense, configurable projector system for RGB-D depth sensors.
	Depth Image Enhancement Intel 2015-08-06 patent / Algorithms to filter, enhance and clean-up RGB-D data streams.
	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.
	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
	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.

	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.
	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.
	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
	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.
	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.
	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.
	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.
	Superresolution Micrscopy Stanford CS371: Computational Biology in Four Dimensions 2017-03-16 code / slides / An implementation of Faster STORM using compressed sensing.
	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.
	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.
	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.
	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.
	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.
	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.
	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
	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.
	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.
	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.
	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.
	GINA: Low power design UC Berkeley 2010-08-22 For testing and validating the functionality of the GINA (Guidance and Inertial NAvigation) mote, a 1.6 gram sensor platform.
	GINA: Wireless sensor platform UC Berkeley 2010-06-22 I helped Anita Flynn and Thomas Watteyne build these small sensors and wrote firmware.