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PointPoseNet: Accurate Object Detection and 6 DoF Pose Estimation in Point Clouds
We present a learning-based method for 6 DoF pose estimation of rigid ob...
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Point Set Voting for Partial Point Cloud Analysis
The continual improvement of 3D sensors has driven the development of al...
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Going Further with Point Pair Features
Point Pair Features is a widely used method to detect 3D objects in poin...
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Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors
In recent years, considerable progress has been made for the task of rig...
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LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching
The local reference frame (LRF) acts as a critical role in 3D local shap...
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Neither Global Nor Local: A Hierarchical Robust Subspace Clustering For Image Data
In this paper, we consider the problem of subspace clustering in presenc...
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A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
This paper proposes a segmentation-free, automatic and efficient procedu...
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3DPVNet: Patch-level 3D Hough Voting Network for 6D Pose Estimation
In this paper, we focus on estimating the 6D pose of objects in point clouds. Although the topic has been widely studied, pose estimation in point clouds remains a challenging problem due to the noise and occlusion. To address the problem, a novel 3DPVNet is presented in this work, which utilizes 3D local patches to vote for the object 6D poses. 3DPVNet is comprised of three modules. In particular, a Patch Unification (PU) module is first introduced to normalize the input patch, and also create a standard local coordinate frame on it to generate a reliable vote. We then devise a Weight-guided Neighboring Feature Fusion (WNFF) module in the network, which fuses the neighboring features to yield a semi-global feature for the center patch. WNFF module mines the neighboring information of a local patch, such that the representation capability to local geometric characteristics is significantly enhanced, making the method robust to a certain level of noise. Moreover, we present a Patch-level Voting (PV) module to regress transformations and generates pose votes. After the aggregation of all votes from patches and a refinement step, the final pose of the object can be obtained. Compared to recent voting-based methods, 3DPVNet is patch-level, and directly carried out on point clouds. Therefore, 3DPVNet achieves less computation than point/pixel-level voting scheme, and has robustness to partial data. Experiments on several datasets demonstrate that 3DPVNet achieves the state-of-the-art performance, and is also robust against noise and occlusions.
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