262 citations · 503 across the 32 of their papers we have counts for
5 papers · 1 filter
Diff-DOPE: Differentiable Deep Object Pose Estimation
Jonathan Tremblay, Bowen Wen, Valts Blukis +3
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. The method uses differentiable rende…
HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions
Andrew Guo, Bowen Wen, Jianhe Yuan +4
We present the HANDAL dataset for category-level object pose estimation and affordance prediction. Unlike previous datasets, ours is focused on robotics-ready manipulable objects t…
Partial-View Object View Synthesis via Filtered Inversion
Fan-Yun Sun, Jonathan Tremblay, Valts Blukis +11
We propose Filtering Inversion (FINV), a learning framework and optimization process that predicts a renderable 3D object representation from one or few partial views. FINV address…
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
Taeyeop Lee, Jonathan Tremblay, Valts Blukis +6
Test-time adaptation methods have been gaining attention recently as a practical solution for addressing source-to-target domain gaps by gradually updating the model without requir…
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
Bowen Wen, Jonathan Tremblay, Valts Blukis +6
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 obj…