Talks and Poster Presentations (with Proceedings-Entry):

K. Park, J. Prankl, M. Vincze:
"Mutual Hypothesis Verification for 6D Pose Estimation of Natural Objects";
Talk: International Conference on Computer Vision, Venedig, Italien; 10-22-2017 - 10-29-2017; in: "ICCV 2017", (2017), 8 pages.

English abstract:
Estimating the 6D pose of natural objects, such as vegetables
and fruit, is a challenging problem due to the high
variability of their shape. The shape variation limits the
accuracy of previous pose estimation approaches because
they assume that the training model and the object in the
target scene have the exact same shape. To overcome this
issue, we propose a novel framework that consists of a local
and a global hypothesis generation pipeline with a mutual
verification step. The new local descriptor is proposed to
find critical parts of the natural object while the global estimator
calculates object pose directly. To determine the best
pose estimation result, a novel hypothesis verification step,
Mutual Hypothesis Verification, is proposed. It interactively
uses information from the local and the global pipelines.
New hypotheses are generated by setting the initial pose using
the global estimation and guiding an iterative closest
point refinement using local shape correspondences. The
confidence of a pose candidate is calculated by comparing
with estimation results from both pipelines. We evaluate
our framework with real fruit randomly piled in a box. The
potential for estimating the pose of any natural object is
proved by the experimental results that outperform global
feature based approaches

Created from the Publication Database of the Vienna University of Technology.