Search Results for author:
Found 5 papers, 0 papers with code
Stable Surface Regularization for Fast Few-Shot NeRF
This paper proposes an algorithm for synthesizing novel views under few-shot setup.
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
Unlike previous unsupervised domain adaptation methods for category-level object pose estimation, our approach processes the test data in a sequential, online manner, and it does not require access to the source domain at runtime.
One-Shot Neural Fields for 3D Object Understanding
At test-time, we build the representation from a single RGB input image observing the scene from only one viewpoint.
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
Inspired by recent multi-modal UDA techniques, the proposed method exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain pose labels.
Ranked #5 on
6D Pose Estimation using RGBD
on REAL275
Category-Level Metric Scale Object Shape and Pose Estimation
Our framework has two branches: the Metric Scale Object Shape branch (MSOS) and the Normalized Object Coordinate Space branch (NOCS).
