9 citations · 25 across the 13 of their papers we have counts for
8 papers · 1 filter
OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB
Yunzhi Lin, Yipu Zhao, Fu-Jen Chu +6
To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mi…
WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis
Yiye Chen, Yunzhi Lin, Ruinian Xu +1
Deep neural networks are susceptible to generating overconfident yet erroneous predictions when presented with data beyond known concepts. This challenge underscores the importance…
Parallel Inversion of Neural Radiance Fields for Robust Pose Estimation
Yunzhi Lin, Thomas Müller, Jonathan Tremblay +5
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. Given a single o…
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…
Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Image
Yunzhi Lin, Jonathan Tremblay, Stephen Tyree +2
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. Category-lev…
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…
A Joint Network for Grasp Detection Conditioned on Natural Language Commands
Yiye Chen, Ruinian Xu, Yunzhi Lin +1
We consider the task of grasping a target object based on a natural language command query. Previous work primarily focused on localizing the object given the query, which requires…
Using Synthetic Data and Deep Networks to Recognize Primitive Shapes for Object Grasping
Yunzhi Lin, Chao Tang, Fu-Jen Chu +1
A segmentation-based architecture is proposed to decompose objects into multiple primitive shapes from monocular depth input for robotic manipulation. The backbone deep network is…