Search Results for author: Stephen Tyree

Found 27 papers, 13 papers with code

Diff-DOPE: Differentiable Deep Object Pose Estimation

no code implementations30 Sep 2023 Jonathan Tremblay, Bowen Wen, Valts Blukis, Balakumar Sundaralingam, Stephen Tyree, Stan Birchfield

We introduce Diff-DOPE, a 6-DoF pose refiner that takes as input an image, a 3D textured model of an object, and an initial pose of the object.

Object Pose Estimation +1

BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects

1 code implementation CVPR 2023 Bowen Wen, Jonathan Tremblay, Valts Blukis, Stephen Tyree, Thomas Muller, Alex Evans, Dieter Fox, Jan Kautz, Stan Birchfield

We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the object.

3D Object Tracking 3D Reconstruction +5

MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare

no code implementations13 Dec 2022 Yann Labbé, Lucas Manuelli, Arsalan Mousavian, Stephen Tyree, Stan Birchfield, Jonathan Tremblay, Justin Carpentier, Mathieu Aubry, Dieter Fox, Josef Sivic

Second, we introduce a novel approach for coarse pose estimation which leverages a network trained to classify whether the pose error between a synthetic rendering and an observed image of the same object can be corrected by the refiner.

6D Pose Estimation Object

Parallel Inversion of Neural Radiance Fields for Robust Pose Estimation

1 code implementation18 Oct 2022 Yunzhi Lin, Thomas Müller, Jonathan Tremblay, Bowen Wen, Stephen Tyree, Alex Evans, Patricio A. Vela, Stan Birchfield

We present a parallelized optimization method based on fast Neural Radiance Fields (NeRF) for estimating 6-DoF pose of a camera with respect to an object or scene.

Pose Estimation

Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation

1 code implementation23 May 2022 Yunzhi Lin, Jonathan Tremblay, Stephen Tyree, Patricio A. Vela, Stan Birchfield

We propose a single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category.

Pose Estimation Pose Tracking

6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark

1 code implementation11 Mar 2022 Stephen Tyree, Jonathan Tremblay, Thang To, Jia Cheng, Terry Mosier, Jeffrey Smith, Stan Birchfield

We propose a set of toy grocery objects, whose physical instantiations are readily available for purchase and are appropriately sized for robotic grasping and manipulation.

Pose Estimation Robotic Grasping

Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Image

1 code implementation13 Sep 2021 Yunzhi Lin, Jonathan Tremblay, Stephen Tyree, Patricio A. Vela, Stan Birchfield

Prior work on 6-DoF object pose estimation has largely focused on instance-level processing, in which a textured CAD model is available for each object being detected.

Object object-detection +2

NViSII: A Scriptable Tool for Photorealistic Image Generation

2 code implementations28 May 2021 Nathan Morrical, Jonathan Tremblay, Yunzhi Lin, Stephen Tyree, Stan Birchfield, Valerio Pascucci, Ingo Wald

We present a Python-based renderer built on NVIDIA's OptiX ray tracing engine and the OptiX AI denoiser, designed to generate high-quality synthetic images for research in computer vision and deep learning.

Image Generation Optical Flow Estimation +1

Indirect Object-to-Robot Pose Estimation from an External Monocular RGB Camera

1 code implementation26 Aug 2020 Jonathan Tremblay, Stephen Tyree, Terry Mosier, Stan Birchfield

We present a robotic grasping system that uses a single external monocular RGB camera as input.


How to Close Sim-Real Gap? Transfer with Segmentation!

no code implementations14 May 2020 Mengyuan Yan, Qingyun Sun, Iuri Frosio, Stephen Tyree, Jan Kautz

Combining the control policy learned from simulation with the perception model, we achieve an impressive $\bf{88\%}$ success rate in grasping a tiny sphere with a real robot.


Importance Estimation for Neural Network Pruning

3 code implementations CVPR 2019 Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, Jan Kautz

On ResNet-101, we achieve a 40% FLOPS reduction by removing 30% of the parameters, with a loss of 0. 02% in the top-1 accuracy on ImageNet.

Network Pruning

Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations

1 code implementation18 May 2018 Jonathan Tremblay, Thang To, Artem Molchanov, Stephen Tyree, Jan Kautz, Stan Birchfield

We present a system to infer and execute a human-readable program from a real-world demonstration.


Improving Landmark Localization with Semi-Supervised Learning

no code implementations CVPR 2018 Sina Honari, Pavlo Molchanov, Stephen Tyree, Pascal Vincent, Christopher Pal, Jan Kautz

First, we propose the framework of sequential multitasking and explore it here through an architecture for landmark localization where training with class labels acts as an auxiliary signal to guide the landmark localization on unlabeled data.

Face Alignment Small Data Image Classification

A Lightweight Approach for On-the-Fly Reflectance Estimation

no code implementations ICCV 2017 Kihwan Kim, Jinwei Gu, Stephen Tyree, Pavlo Molchanov, Matthias Nießner, Jan Kautz

In addition, we have created a large synthetic dataset, SynBRDF, which comprises a total of $500$K RGBD images rendered with a physically-based ray tracer under a variety of natural illumination, covering $5000$ materials and $5000$ shapes.

Color Constancy

Pruning Convolutional Neural Networks for Resource Efficient Inference

9 code implementations19 Nov 2016 Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, Jan Kautz

We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters.

Transfer Learning

Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU

3 code implementations18 Nov 2016 Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz

We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks.

reinforcement-learning Reinforcement Learning (RL) +1

Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network

no code implementations CVPR 2016 Pavlo Molchanov, Xiaodong Yang, Shalini Gupta, Kihwan Kim, Stephen Tyree, Jan Kautz

Automatic detection and classification of dynamic hand gestures in real-world systems intended for human computer interaction is challenging as: 1) there is a large diversity in how people perform gestures, making detection and classification difficult; 2) the system must work online in order to avoid noticeable lag between performing a gesture and its classification; in fact, a negative lag (classification before the gesture is finished) is desirable, as feedback to the user can then be truly instantaneous.

Classification General Classification +1

Compressing Convolutional Neural Networks

no code implementations14 Jun 2015 Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, Yixin Chen

Convolutional neural networks (CNN) are increasingly used in many areas of computer vision.

Compressing Neural Networks with the Hashing Trick

1 code implementation19 Apr 2015 Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, Yixin Chen

As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models.

Compressed Support Vector Machines

no code implementations26 Jan 2015 Zhixiang Xu, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger

For most of the time during which we conducted this research, we were unaware of this prior work.

Image Data Compression for Covariance and Histogram Descriptors

no code implementations4 Dec 2014 Matt J. Kusner, Nicholas I. Kolkin, Stephen Tyree, Kilian Q. Weinberger

Specifically, we show that we can reduce data sets to 16% and in some cases as little as 2% of their original size, while approximately matching the test error of kNN classification on the full training set.

Data Compression General Classification

Parallel Support Vector Machines in Practice

no code implementations3 Apr 2014 Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

In particular, we provide the first comparison of algorithms with explicit and implicit parallelization.

Marginalizing Corrupted Features

no code implementations27 Feb 2014 Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Weinberger

In this paper, we propose a third, alternative approach to combat overfitting: we extend the training set with infinitely many artificial training examples that are obtained by corrupting the original training data.

Bayesian Inference

Non-linear Metric Learning

no code implementations NeurIPS 2012 Dor Kedem, Stephen Tyree, Fei Sha, Gert R. Lanckriet, Kilian Q. Weinberger

On various benchmark data sets, we demonstrate these methods not only match the current state-of-the-art in terms of kNN classification error, but in the case of χ2-LMNN, obtain best results in 19 out of 20 learning settings.

Metric Learning

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