2,559 research outputs found
Regularization Based Iterative Point Match Weighting for Accurate Rigid Transformation Estimation
Feature extraction and matching (FEM) for 3D shapes finds numerous applications in computer graphics and vision for object modeling, retrieval, morphing, and recognition. However, unavoidable incorrect matches lead to inaccurate estimation of the transformation relating different datasets. Inspired by AdaBoost, this paper proposes a novel iterative re-weighting method to tackle the challenging problem of evaluating point matches established by typical FEM methods. Weights are used to indicate the degree of belief that each point match is correct. Our method has three key steps: (i) estimation of the underlying transformation using weighted least squares, (ii) penalty parameter estimation via minimization of the weighted variance of the matching errors, and (iii) weight re-estimation taking into account both matching errors and information learnt in previous iterations. A comparative study, based on real shapes captured by two laser scanners, shows that the proposed method outperforms four other state-of-the-art methods in terms of evaluating point matches between overlapping shapes established by two typical FEM methods, resulting in more accurate estimates of the underlying transformation. This improved transformation can be used to better initialize the iterative closest point algorithm and its variants, making 3D shape registration more likely to succeed
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
The combination of aerial survey capabilities of Unmanned Aerial Vehicles
with targeted intervention abilities of agricultural Unmanned Ground Vehicles
can significantly improve the effectiveness of robotic systems applied to
precision agriculture. In this context, building and updating a common map of
the field is an essential but challenging task. The maps built using robots of
different types show differences in size, resolution and scale, the associated
geolocation data may be inaccurate and biased, while the repetitiveness of both
visual appearance and geometric structures found within agricultural contexts
render classical map merging techniques ineffective. In this paper we propose
AgriColMap, a novel map registration pipeline that leverages a grid-based
multimodal environment representation which includes a vegetation index map and
a Digital Surface Model. We cast the data association problem between maps
built from UAVs and UGVs as a multimodal, large displacement dense optical flow
estimation. The dominant, coherent flows, selected using a voting scheme, are
used as point-to-point correspondences to infer a preliminary non-rigid
alignment between the maps. A final refinement is then performed, by exploiting
only meaningful parts of the registered maps. We evaluate our system using real
world data for 3 fields with different crop species. The results show that our
method outperforms several state of the art map registration and matching
techniques by a large margin, and has a higher tolerance to large initial
misalignments. We release an implementation of the proposed approach along with
the acquired datasets with this paper.Comment: Published in IEEE Robotics and Automation Letters, 201
Convex Relaxations of SE(2) and SE(3) for Visual Pose Estimation
This paper proposes a new method for rigid body pose estimation based on
spectrahedral representations of the tautological orbitopes of and
. The approach can use dense point cloud data from stereo vision or an
RGB-D sensor (such as the Microsoft Kinect), as well as visual appearance data.
The method is a convex relaxation of the classical pose estimation problem, and
is based on explicit linear matrix inequality (LMI) representations for the
convex hulls of and . Given these representations, the relaxed
pose estimation problem can be framed as a robust least squares problem with
the optimization variable constrained to these convex sets. Although this
formulation is a relaxation of the original problem, numerical experiments
indicate that it is indeed exact - i.e. its solution is a member of or
- in many interesting settings. We additionally show that this method
is guaranteed to be exact for a large class of pose estimation problems.Comment: ICRA 2014 Preprin
DeepMatching: Hierarchical Deformable Dense Matching
We introduce a novel matching algorithm, called DeepMatching, to compute
dense correspondences between images. DeepMatching relies on a hierarchical,
multi-layer, correlational architecture designed for matching images and was
inspired by deep convolutional approaches. The proposed matching algorithm can
handle non-rigid deformations and repetitive textures and efficiently
determines dense correspondences in the presence of significant changes between
images. We evaluate the performance of DeepMatching, in comparison with
state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al
2005), the MPI-Sintel (Butler et al 2012) and the Kitti (Geiger et al 2013)
datasets. DeepMatching outperforms the state-of-the-art algorithms and shows
excellent results in particular for repetitive textures.We also propose a
method for estimating optical flow, called DeepFlow, by integrating
DeepMatching in the large displacement optical flow (LDOF) approach of Brox and
Malik (2011). Compared to existing matching algorithms, additional robustness
to large displacements and complex motion is obtained thanks to our matching
approach. DeepFlow obtains competitive performance on public benchmarks for
optical flow estimation
LO-Net: Deep Real-time Lidar Odometry
We present a novel deep convolutional network pipeline, LO-Net, for real-time
lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations
that go through individually designed feature selection, feature matching, and
pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a
new mask-weighted geometric constraint loss, LO-Net can effectively learn
feature representation for LO estimation, and can implicitly exploit the
sequential dependencies and dynamics in the data. We also design a scan-to-map
module, which uses the geometric and semantic information learned in LO-Net, to
improve the estimation accuracy. Experiments on benchmark datasets demonstrate
that LO-Net outperforms existing learning based approaches and has similar
accuracy with the state-of-the-art geometry-based approach, LOAM
Finite element surface registration incorporating curvature, volume preservation, and statistical model information
We present a novel method for nonrigid registration of 3D surfaces and images. The method can be used to register surfaces by means of their distance images, or to register medical images directly. It is formulated as a minimization problem of a sum of several terms representing the desired properties of a registration result: smoothness, volume preservation, matching of the surface, its curvature, and possible other feature images, as well as consistency with previous registration results of similar objects, represented by a statistical deformation model. While most of these concepts are already known, we present a coherent continuous formulation of these constraints, including the statistical deformation model. This continuous formulation renders the registration method independent of its discretization. The finite element discretization we present is, while independent of the registration functional, the second main contribution of this paper. The local discontinuous Galerkin method has not previously been used in image registration, and it provides an efficient and general framework to discretize each of the terms of our functional. Computational efficiency and modest memory consumption are achieved thanks to parallelization and locally adaptive mesh refinement. This allows for the first time the use of otherwise prohibitively large 3D statistical deformation models
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
In this paper, we study the problem of 3D object segmentation from raw point
clouds. Unlike all existing methods which usually require a large amount of
human annotations for full supervision, we propose the first unsupervised
method, called OGC, to simultaneously identify multiple 3D objects in a single
forward pass, without needing any type of human annotations. The key to our
approach is to fully leverage the dynamic motion patterns over sequential point
clouds as supervision signals to automatically discover rigid objects. Our
method consists of three major components, 1) the object segmentation network
to directly estimate multi-object masks from a single point cloud frame, 2) the
auxiliary self-supervised scene flow estimator, and 3) our core object geometry
consistency component. By carefully designing a series of loss functions, we
effectively take into account the multi-object rigid consistency and the object
shape invariance in both temporal and spatial scales. This allows our method to
truly discover the object geometry even in the absence of annotations. We
extensively evaluate our method on five datasets, demonstrating the superior
performance for object part instance segmentation and general object
segmentation in both indoor and the challenging outdoor scenarios.Comment: NeurIPS 2022. Code and data are available at:
https://github.com/vLAR-group/OG
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