Optimal Pose and Shape Estimation for Category-level 3D Object Perception
Name
2104.08383.pdf
Description
Accepted version
Size
8.13 MB
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Unknown
Checksum (MD5)
a1c5951b072068e8d37b9e6b27093b75
Author(s)
Shi, Jingnan
Yang, Heng
Carlone, Luca
Date Issued
2021
Journal
Robotics: Science and Systems XVII
Publisher
Robotics: Science and Systems Foundation
Citation
Shi, Jingnan, Yang, Heng and Carlone, Luca. 2021. "Optimal Pose and Shape Estimation for Category-level 3D Object Perception." Robotics: Science and Systems XVII.
Version
Author's final manuscript
Abstract
We consider a category-level perception problem,
where one is given 3D sensor data picturing an object of a given
category (e.g., a car), and has to reconstruct the pose and shape of
the object despite intra-class variability (i.e., different car models
have different shapes). We consider an active shape model, where
—for an object category— we are given a library of potential
CAD models describing objects in that category, and we adopt
a standard formulation where pose and shape estimation are
formulated as a non-convex optimization. Our first contribution
is to provide the first certifiably optimal solver for pose and shape
estimation. In particular, we show that rotation estimation can
be decoupled from the estimation of the object translation and
shape, and we demonstrate that (i) the optimal object rotation
can be computed via a tight (small-size) semidefinite relaxation,
and (ii) the translation and shape parameters can be computed in
closed-form given the rotation. Our second contribution is to add
an outlier rejection layer to our solver, hence making it robust to
a large number of misdetections. Towards this goal, we wrap our
optimal solver in a robust estimation scheme based on graduated
non-convexity. To further enhance robustness to outliers, we also
develop the first graph-theoretic formulation to prune outliers
in category-level perception, which removes outliers via convex
hull and maximum clique computations; the resulting approach
is robust to 70 − 90% outliers. Our third contribution is an
extensive experimental evaluation. Besides providing an ablation
study on a simulated dataset and on the PASCAL3D+ dataset, we
combine our solver with a deep-learned keypoint detector, and
show that the resulting approach improves over the state of the
art in vehicle pose estimation in the ApolloScape datasets.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.15607/RSS.2021.XVII.025
