Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds

• URL: http://arxiv.org/abs/2106.16129v1
• Date: Wed, 30 Jun 2021 15:26:15 GMT
• Title: Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds
• Authors: Mihaela C\u{a}t\u{a}lina Stoian, Tommaso Cavallari
• Abstract summary: We present an alternative novel encoding that instead slices the data along the height dimension and passes it sequentially to a 2D convolutional recurrent regression scheme. We show that our approach has an accuracy comparable to state-of-the-art techniques on the task of planar reflective symmetry estimation on full synthetic objects.
• Score: 5.098175145801009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many man-made objects are characterised by a shape that is symmetric along one or more planar directions. Estimating the location and orientation of such symmetry planes can aid many tasks such as estimating the overall orientation of an object of interest or performing shape completion, where a partial scan of an object is reflected across the estimated symmetry plane in order to obtain a more detailed shape. Many methods processing 3D data rely on expensive 3D convolutions. In this paper we present an alternative novel encoding that instead slices the data along the height dimension and passes it sequentially to a 2D convolutional recurrent regression scheme. The method also comprises a differentiable least squares step, allowing for end-to-end accurate and fast processing of both full and partial scans of symmetric objects. We use this approach to efficiently handle 3D inputs to design a method to estimate planar reflective symmetries. We show that our approach has an accuracy comparable to state-of-the-art techniques on the task of planar reflective symmetry estimation on full synthetic objects. Additionally, we show that it can be deployed on partial scans of objects in a real-world pipeline to improve the outputs of a 3D object detector.

Related papers

• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices. Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• Partial Symmetry Detection for 3D Geometry using Contrastive Learning with Geodesic Point Cloud Patches [10.48309709793733]
  We propose to learn rotation, reflection, translation and scale invariant local shape features for geodesic point cloud patches. We show that our approach is able to extract multiple valid solutions for this ambiguous problem. We incorporate the detected symmetries together with a region growing algorithm to demonstrate a downstream task.
  arXiv  Detail & Related papers  (2023-12-13T15:48:50Z)
• Neural Semantic Surface Maps [52.61017226479506]
  We present an automated technique for computing a map between two genus-zero shapes, which matches semantically corresponding regions to one another. Our approach can generate semantic surface-to-surface maps, eliminating manual annotations or any 3D training data requirement.
  arXiv  Detail & Related papers  (2023-09-09T16:21:56Z)
• FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models [67.96827539201071]
  We propose a novel test-time optimization approach for 3D scene reconstruction. Our method achieves state-of-the-art cross-dataset reconstruction on five zero-shot testing datasets.
  arXiv  Detail & Related papers  (2023-08-10T17:55:02Z)
• Data-efficient learning for 3D mirror symmetry detection [9.904746542801838]
  We introduce a geometry-inspired deep learning method for detecting 3D mirror plane from single-view images. We extract semantic features, calculate intra-pixel correlations, and build a 3D correlation volume for each plane. Experiments on both synthetic and real-world datasets show the benefit of 3D mirror geometry in improving data efficiency and inference speed.
  arXiv  Detail & Related papers  (2021-12-23T14:37:52Z)
• Geometry-Contrastive Transformer for Generalized 3D Pose Transfer [95.56457218144983]
  The intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. We propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies. We present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task.
  arXiv  Detail & Related papers  (2021-12-14T13:14:24Z)
• Robust Extrinsic Symmetry Estimation in 3D Point Clouds [4.416484585765027]
  Detecting the reflection symmetry plane of an object represented by a 3D point cloud is a fundamental problem in 3D computer vision and geometry processing. We propose a statistical estimator-based approach for the plane of reflection symmetry that is robust to outliers and missing parts.
  arXiv  Detail & Related papers  (2021-09-21T03:09:51Z)
• DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension [71.71234436165255]
  We contribute DensePose 3D, a method that can learn such reconstructions in a weakly supervised fashion from 2D image annotations only. Because it does not require 3D scans, DensePose 3D can be used for learning a wide range of articulated categories such as different animal species. We show significant improvements compared to state-of-the-art non-rigid structure-from-motion baselines on both synthetic and real data on categories of humans and animals.
  arXiv  Detail & Related papers  (2021-08-31T18:33:55Z)
• NeRD: Neural 3D Reflection Symmetry Detector [27.626579746101292]
  We present NeRD, a Neural 3D Reflection Symmetry Detector. We first enumerate the symmetry planes with a coarse-to-fine strategy and then find the best ones by building 3D cost volumes. Our experiments show that the symmetry planes detected with our method are significantly more accurate than the planes from direct CNN regression.
  arXiv  Detail & Related papers  (2021-04-19T17:25:51Z)
• From Points to Multi-Object 3D Reconstruction [71.17445805257196]
  We propose a method to detect and reconstruct multiple 3D objects from a single RGB image. A keypoint detector localizes objects as center points and directly predicts all object properties, including 9-DoF bounding boxes and 3D shapes. The presented approach performs lightweight reconstruction in a single-stage, it is real-time capable, fully differentiable and end-to-end trainable.
  arXiv  Detail & Related papers  (2020-12-21T18:52:21Z)
• SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images [26.38270361331076]
  We propose an end-to-end deep neural network which is able to predict both reflectional and rotational symmetries of 3D objects. We also contribute a benchmark of 3D symmetry detection based on single-view RGB-D images.
  arXiv  Detail & Related papers  (2020-08-02T14:10:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.