Search Results for author: Stan Birchfield

Found 32 papers, 17 papers with code

Vicinity Vision Transformer

1 code implementation21 Jun 2022 Weixuan Sun, Zhen Qin, Hui Deng, Jianyuan Wang, Yi Zhang, Kaihao Zhang, Nick Barnes, Stan Birchfield, Lingpeng Kong, Yiran Zhong

Based on this observation, we present a Vicinity Attention that introduces a locality bias to vision transformers with linear complexity.

Computer Vision Image Classification +1

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

RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis

no code implementations14 May 2022 Jonathan Tremblay, Moustafa Meshry, Alex Evans, Jan Kautz, Alexander Keller, Sameh Khamis, Charles Loop, Nathan Morrical, Koki Nagano, Towaki Takikawa, Stan Birchfield

We present a large-scale synthetic dataset for novel view synthesis consisting of ~300k images rendered from nearly 2000 complex scenes using high-quality ray tracing at high resolution (1600 x 1600 pixels).

Novel View Synthesis

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.

Computer Vision Pose Estimation +1

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.

2D object detection 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.

Computer Vision Image Generation +2

Self-Supervised Real-to-Sim Scene Generation

no code implementations ICCV 2021 Aayush Prakash, Shoubhik Debnath, Jean-Francois Lafleche, Eric Cameracci, Gavriel State, Stan Birchfield, Marc T. Law

Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate.

Graph Generation Scene Generation +3

Fast Uncertainty Quantification for Deep Object Pose Estimation

no code implementations16 Nov 2020 Guanya Shi, Yifeng Zhu, Jonathan Tremblay, Stan Birchfield, Fabio Ramos, Animashree Anandkumar, Yuke Zhu

Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transfer.

Pose Estimation

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.


Improving Deep Stereo Network Generalization with Geometric Priors

no code implementations25 Aug 2020 Jialiang Wang, Varun Jampani, Deqing Sun, Charles Loop, Stan Birchfield, Jan Kautz

End-to-end deep learning methods have advanced stereo vision in recent years and obtained excellent results when the training and test data are similar.

MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views

no code implementations9 Jun 2020 Ke Chen, Ryan Oldja, Nikolai Smolyanskiy, Stan Birchfield, Alexander Popov, David Wehr, Ibrahim Eden, Joachim Pehserl

We show that our multi-view, multi-stage, multi-class approach is able to detect and classify objects while simultaneously determining the drivable space using a single LiDAR scan as input, in challenging scenes with more than one hundred vehicles and pedestrians at a time.

Autonomous Driving object-detection +2

Camera-to-Robot Pose Estimation from a Single Image

2 code implementations21 Nov 2019 Timothy E. Lee, Jonathan Tremblay, Thang To, Jia Cheng, Terry Mosier, Oliver Kroemer, Dieter Fox, Stan Birchfield

We show experimental results for three different camera sensors, demonstrating that our approach is able to achieve accuracy with a single frame that is better than that of classic off-line hand-eye calibration using multiple frames.


Contextual Reinforcement Learning of Visuo-tactile Multi-fingered Grasping Policies

no code implementations21 Nov 2019 Visak Kumar, Tucker Herman, Dieter Fox, Stan Birchfield, Jonathan Tremblay

We propose a Grasping Objects Approach for Tactile (GOAT) robotic hands, learning to overcome the reality gap problem.


DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System

no code implementations7 Oct 2019 Ankur Handa, Karl Van Wyk, Wei Yang, Jacky Liang, Yu-Wei Chao, Qian Wan, Stan Birchfield, Nathan Ratliff, Dieter Fox

Teleoperation offers the possibility of imparting robotic systems with sophisticated reasoning skills, intuition, and creativity to perform tasks.

Few-Shot Viewpoint Estimation

no code implementations13 May 2019 Hung-Yu Tseng, Shalini De Mello, Jonathan Tremblay, Sifei Liu, Stan Birchfield, Ming-Hsuan Yang, Jan Kautz

Through extensive experimentation on the ObjectNet3D and Pascal3D+ benchmark datasets, we demonstrate that our framework, which we call MetaView, significantly outperforms fine-tuning the state-of-the-art models with few examples, and that the specific architectural innovations of our method are crucial to achieving good performance.

Meta-Learning Viewpoint Estimation

RMPflow: A Computational Graph for Automatic Motion Policy Generation

1 code implementation16 Nov 2018 Ching-An Cheng, Mustafa Mukadam, Jan Issac, Stan Birchfield, Dieter Fox, Byron Boots, Nathan Ratliff

We develop a novel policy synthesis algorithm, RMPflow, based on geometrically consistent transformations of Riemannian Motion Policies (RMPs).

Robotics Systems and Control

Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects

7 code implementations27 Sep 2018 Jonathan Tremblay, Thang To, Balakumar Sundaralingam, Yu Xiang, Dieter Fox, Stan Birchfield

Using synthetic data generated in this manner, we introduce a one-shot deep neural network that is able to perform competitively against a state-of-the-art network trained on a combination of real and synthetic data.


Region Growing Curriculum Generation for Reinforcement Learning

no code implementations4 Jul 2018 Artem Molchanov, Karol Hausman, Stan Birchfield, Gaurav Sukhatme

In this work, we introduce a method based on region-growing that allows learning in an environment with any pair of initial and goal states.


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.


Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation

no code implementations18 Apr 2018 Jonathan Tremblay, Thang To, Stan Birchfield

We present a new dataset, called Falling Things (FAT), for advancing the state-of-the-art in object detection and 3D pose estimation in the context of robotics.

3D Object Detection 3D Pose Estimation +1

On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach

4 code implementations26 Mar 2018 Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield

Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large$-$a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving.

Autonomous Vehicles Stereo Depth Estimation

Efficient Hierarchical Graph-Based Segmentation of RGBD Videos

1 code implementation CVPR 2014 Steven Hickson, Stan Birchfield, Irfan Essa, Henrik Christensen

We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach.

Graph Matching Video Segmentation

An Energy Minimization Approach to 3D Non-Rigid Deformable Surface Estimation Using RGBD Data

no code implementations2 Aug 2017 Bryan Willimon, Steven Hickson, Ian Walker, Stan Birchfield

In particular, we show that our method is able to estimate the configuration of a textureless nonrigid object with no correspondences available.

Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness

4 code implementations7 May 2017 Nikolai Smolyanskiy, Alexey Kamenev, Jeffrey Smith, Stan Birchfield

We present a micro aerial vehicle (MAV) system, built with inexpensive off-the-shelf hardware, for autonomously following trails in unstructured, outdoor environments such as forests.


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