70 citations · 223 across the 26 of their papers we have counts for
5 papers · 2 filters
MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare
Yann Labbé, Lucas Manuelli, Arsalan Mousavian +7
We introduce MegaPose, a method to estimate the 6D pose of novel objects, that is, objects unseen during training. At inference time, the method only assumes knowledge of (i) a reg…
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…
Variable Bitrate Neural Fields
Towaki Takikawa, Alex Evans, Jonathan Tremblay +4
Neural approximations of scalar and vector fields, such as signed distance functions and radiance fields, have emerged as accurate, high-quality representations. State-of-the-art r…
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…
RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis
Jonathan Tremblay, Moustafa Meshry, Alex Evans +9
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 resol…