VASC Seminar
Zach Pezzementi
Lead Robotics Engineer
Carnegie Mellon University / NREC

Comparing apples and oranges: Off-road pedestrian detection on the NREC agricultural person-detection dataset

GHC 6501

Abstract: Person detection from vehicles has made rapid progress recently with the advent of multiple high-quality datasets of urban and highway driving, yet no large-scale benchmark has been available for the same problem in off-road or agricultural environments. In this talk, we present the NREC Agricultural Person-Detection Dataset to spur research in these environments. It [...]

VASC Seminar
Debadeepta Dey
Researcher
Microsoft Research AI (MSR AI)

Adaptive Information Gathering via Imitation Learning

GHC 6501

Abstract: In the adaptive information gathering problem, a robot is required to select an informative sensing location using the history of measurements acquired thus far. While there is an extensive amount of prior work investigating effective practical approximations using variants of Shannon’s entropy, the efficacy of such policies heavily depends on the geometric distribution of [...]

RI Seminar
Greg Mori
Professor
School of Computer Science, Simon Fraser University

Deep Structured Models for Human Activity Recognition

1305 Newell Simon Hall

Abstract: Visual recognition involves reasoning about structured relations at multiple levels of detail.  For example, human behaviour analysis requires a comprehensive labeling covering individual low-level actions to pair-wise interactions through to high-level events.  Scene understanding can benefit from considering labels and their inter-relations.  In this talk I will present recent work by our group building [...]

VASC Seminar
Shubham Tulsiani
PhD Candidate
UC, Berkeley

Learning Single-view 3D Reconstruction of Objects and Scenes

GHC 6501

Abstract: In this talk, I will discuss the task of inferring 3D structure underlying an image, in particular focusing on two questions - a) how we can plausibly obtain supervisory signal for this task, and b) what forms of representation should we pursue. I will first show that we can leverage image-based supervision to learn [...]

Field Robotics Center Seminar
Systems Scientist
Robotics Institute,
Carnegie Mellon University

From Robust Real-time SLAM to Safe Collision Avoidance

Newell Simon Hall 1507

Abstract State estimation plays a critical role in a robotic system. The problem is to know where the robot is and how it is oriented. This is very often a building block in the navigation system, which modules in charge of higher level tasks are relied on. Challenges are to carry out state estimation in [...]

RI Seminar
David Breen
Associate Professor
Department of Computer Science, Drexel University

Level Set Models for Computer Graphics

1305 Newell Simon Hall

ABSTRACT A level set model is a deformable implicit model that has a regularly-sampled representation.  It is defined as an iso-contour, i.e. a level set, of some implicit function f.  The contour is deformed by solving a partial differential equation on a sampling of f, an image in 2D and a volume dataset in 3D.  [...]

VASC Seminar
Ryad Benosman
Professor
University Pierre and Marie Curie, Paris

Neuromorphic Event-based time oriented vision and Computation

GHC 6501

Abstract: There has been significant research over the past two decades in developing new systems for spiking neural computation. The impact of neuromorphic concepts on recent developments in optical sensing, display and artificial vision is presented. State-of-the-art image sensors suffer from severe limitations imposed by their very principle of operation. These sensors acquire the visual [...]

RI Seminar
Prof. Dr.-Ing. Michael Goesele
Professor
Graphics, Capture and Massively Parallel Computing , Technische Universität Darmstadt

“Does it look right? – Why capture and reconstruction quality really matter.”

1305 Newell Simon Hall

Special RI Seminar Please Note Different Day and Time Abstract:  At first sight, 3D reconstruction can be considered a solved problem. The principles are well understood and we can reconstruct a wide range of objects and scenes using active as well as passive reconstruction approached. However, most of these reconstructions are not convincing when really [...]

RI Seminar
Frank Dellaert
Technical Project Lead at Building 8
Facebook

Factor Graphs and Automatic Differentiation for Flexible Inference in Robotics and Vision

1305 Newell Simon Hall

PLEASE NOTE: THIS SEMINAR WILL NOT BE RECORDED Abstract: Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SFM) are important and closely related problems in robotics and vision. I will review how SLAM, SFM and other problems in robotics and vision can be posed in terms of factor graphs, which provide a graphical language [...]