232 research outputs found
Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition
It is possible to associate a highly constrained subset of relative 6 DoF
poses between two 3D shapes, as long as the local surface orientation, the
normal vector, is available at every surface point. Local shape features can be
used to find putative point correspondences between the models due to their
ability to handle noisy and incomplete data. However, this correspondence set
is usually contaminated by outliers in practical scenarios, which has led to
many past contributions based on robust detectors such as the Hough transform
or RANSAC. The key insight of our work is that a single correspondence between
oriented points on the two models is constrained to cast votes in a 1 DoF
rotational subgroup of the full group of poses, SE(3). Kernel density
estimation allows combining the set of votes efficiently to determine a full 6
DoF candidate pose between the models. This modal pose with the highest density
is stable under challenging conditions, such as noise, clutter, and occlusions,
and provides the output estimate of our method.
We first analyze the robustness of our method in relation to noise and show
that it handles high outlier rates much better than RANSAC for the task of 6
DoF pose estimation. We then apply our method to four state of the art data
sets for 3D object recognition that contain occluded and cluttered scenes. Our
method achieves perfect recall on two LIDAR data sets and outperforms competing
methods on two RGB-D data sets, thus setting a new standard for general 3D
object recognition using point cloud data.Comment: Accepted for International Conference on Computer Vision (ICCV), 201
6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features
The point pair feature (PPF) is widely used for 6D pose estimation. In this
paper, we propose an efficient 6D pose estimation method based on the PPF
framework. We introduce a well-targeted down-sampling strategy that focuses
more on edge area for efficient feature extraction of complex geometry. A pose
hypothesis validation approach is proposed to resolve the symmetric ambiguity
by calculating edge matching degree. We perform evaluations on two challenging
datasets and one real-world collected dataset, demonstrating the superiority of
our method on pose estimation of geometrically complex, occluded, symmetrical
objects. We further validate our method by applying it to simulated punctures.Comment: 16 pages,20 figure
I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting
3D object classification has attracted appealing attentions in academic
researches and industrial applications. However, most existing methods need to
access the training data of past 3D object classes when facing the common
real-world scenario: new classes of 3D objects arrive in a sequence. Moreover,
the performance of advanced approaches degrades dramatically for past learned
classes (i.e., catastrophic forgetting), due to the irregular and redundant
geometric structures of 3D point cloud data. To address these challenges, we
propose a new Incremental 3D Object Learning (i.e., I3DOL) model, which is the
first exploration to learn new classes of 3D object continually. Specifically,
an adaptive-geometric centroid module is designed to construct discriminative
local geometric structures, which can better characterize the irregular point
cloud representation for 3D object. Afterwards, to prevent the catastrophic
forgetting brought by redundant geometric information, a geometric-aware
attention mechanism is developed to quantify the contributions of local
geometric structures, and explore unique 3D geometric characteristics with high
contributions for classes incremental learning. Meanwhile, a score fairness
compensation strategy is proposed to further alleviate the catastrophic
forgetting caused by unbalanced data between past and new classes of 3D object,
by compensating biased prediction for new classes in the validation phase.
Experiments on 3D representative datasets validate the superiority of our I3DOL
framework.Comment: Accepted by Association for the Advancement of Artificial
Intelligence 2021 (AAAI 2021
Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation
We introduce a novel method for robust and accurate 3D object pose estimation
from a single color image under large occlusions. Following recent approaches,
we first predict the 2D projections of 3D points related to the target object
and then compute the 3D pose from these correspondences using a geometric
method. Unfortunately, as the results of our experiments show, predicting these
2D projections using a regular CNN or a Convolutional Pose Machine is highly
sensitive to partial occlusions, even when these methods are trained with
partially occluded examples. Our solution is to predict heatmaps from multiple
small patches independently and to accumulate the results to obtain accurate
and robust predictions. Training subsequently becomes challenging because
patches with similar appearances but different positions on the object
correspond to different heatmaps. However, we provide a simple yet effective
solution to deal with such ambiguities. We show that our approach outperforms
existing methods on two challenging datasets: The Occluded LineMOD dataset and
the YCB-Video dataset, both exhibiting cluttered scenes with highly occluded
objects. Project website:
https://www.tugraz.at/institute/icg/research/team-lepetit/research-projects/robust-object-pose-estimation
Symmetry Detection in Large Scale City Scans
In this report we present a novel method for detecting partial symmetries in very large point clouds of 3D city scans. Unlike previous work, which was limited to data sets of a few hundred megabytes maximum, our method scales to very large scenes. We map the detection problem to a nearestneighbor search in a low-dimensional feature space, followed by a cascade of tests for geometric clustering of potential matches. Our algorithm robustly handles noisy real-world scanner data, obtaining a recognition performance comparable to state-of-the-art methods. In practice, it scales linearly with the scene size and achieves a high absolute throughput, processing half a terabyte of raw scanner data over night on a dual socket commodity PC
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