44 citations · 79 across the 10 of their papers we have counts for
7 papers · 1 filter
Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation
Yunzhi Lin, Jonathan Tremblay, Stephen Tyree +2
We propose a single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category. Our method takes as i…
NViSII: A Scriptable Tool for Photorealistic Image Generation
Nathan Morrical, Jonathan Tremblay, Yunzhi Lin +4
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…
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
Yu-Wei Chao, Wei Yang, Yu Xiang +9
We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first compare DexYCB with a related one through cross-dataset evaluation. We then present a thorough b…
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data
Zheng Tang, Milind Naphade, Stan Birchfield +5
In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention. Vehicle ReID is challenging d…
Few-Shot Viewpoint Estimation
Hung-Yu Tseng, Shalini De Mello, Jonathan Tremblay +4
Viewpoint estimation for known categories of objects has been improved significantly thanks to deep networks and large datasets, but generalization to unknown categories is still v…
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
Jonathan Tremblay, Aayush Prakash, David Acuna +7
We present a system for training deep neural networks for object detection using synthetic images. To handle the variability in real-world data, the system relies upon the techniqu…