One Point, One Object: Simultaneous 3D Object Segmentation and 6-DOF Pose Estimation

• URL: http://arxiv.org/abs/1912.12095v2
• Date: Thu, 6 Jun 2024 08:28:13 GMT
• Title: One Point, One Object: Simultaneous 3D Object Segmentation and 6-DOF Pose Estimation
• Authors: Hongsen Liu,
• Abstract summary: We propose a method for simultaneous 3D object segmentation and 6-DOF pose estimation in pure 3D point clouds scenes. The key component of our method is a multi-task CNN architecture that can simultaneously predict the 3D object segmentation and 6-DOF pose estimation in pure 3D point clouds. For experimental evaluation, we generate expanded training data for two state-of-the-arts 3D object datasets citePLciteTLINEMOD by using Augmented Reality (AR)
• Score: 0.7252027234425334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a single-shot method for simultaneous 3D object segmentation and 6-DOF pose estimation in pure 3D point clouds scenes based on a consensus that \emph{one point only belongs to one object}, i.e., each point has the potential power to predict the 6-DOF pose of its corresponding object. Unlike the recently proposed methods of the similar task, which rely on 2D detectors to predict the projection of 3D corners of the 3D bounding boxes and the 6-DOF pose must be estimated by a PnP like spatial transformation method, ours is concise enough not to require additional spatial transformation between different dimensions. Due to the lack of training data for many objects, the recently proposed 2D detection methods try to generate training data by using rendering engine and achieve good results. However, rendering in 3D space along with 6-DOF is relatively difficult. Therefore, we propose an augmented reality technology to generate the training data in semi-virtual reality 3D space. The key component of our method is a multi-task CNN architecture that can simultaneously predicts the 3D object segmentation and 6-DOF pose estimation in pure 3D point clouds. For experimental evaluation, we generate expanded training data for two state-of-the-arts 3D object datasets \cite{PLCHF}\cite{TLINEMOD} by using Augmented Reality technology (AR). We evaluate our proposed method on the two datasets. The results show that our method can be well generalized into multiple scenarios and provide performance comparable to or better than the state-of-the-arts.

Related papers

• OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data [15.53270401654078]
  OVIR-3D is a method for open-vocabulary 3D object instance retrieval without using any 3D data for training. It is achieved by a multi-view fusion of text-aligned 2D region proposals into 3D space. Experiments on public datasets and a real robot show the effectiveness of the method and its potential for applications in robot navigation and manipulation.
  arXiv  Detail & Related papers  (2023-11-06T05:00:00Z)
• 6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics [19.64111218032901]
  We present a novel technique to estimate the 6D pose of objects from single images. We employ a dense 2D-to-3D correspondence predictor that regresses 3D model coordinates for every pixel. Our method achieves state-of-the-art performance on the SPEED+ dataset and has won the SPEC2021 post-mortem competition.
  arXiv  Detail & Related papers  (2023-03-23T13:18:05Z)
• Neural Correspondence Field for Object Pose Estimation [67.96767010122633]
  We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict 3D object coordinates at pixels of the input image, the proposed method predicts 3D object coordinates at 3D query points sampled in the camera frustum.
  arXiv  Detail & Related papers  (2022-07-30T01:48:23Z)
• Homography Loss for Monocular 3D Object Detection [54.04870007473932]
  A differentiable loss function, termed as Homography Loss, is proposed to achieve the goal, which exploits both 2D and 3D information. Our method yields the best performance compared with the other state-of-the-arts by a large margin on KITTI 3D datasets.
  arXiv  Detail & Related papers  (2022-04-02T03:48:03Z)
• Weakly Supervised Learning of Keypoints for 6D Object Pose Estimation [73.40404343241782]
  We propose a weakly supervised 6D object pose estimation approach based on 2D keypoint detection. Our approach achieves comparable performance with state-of-the-art fully supervised approaches.
  arXiv  Detail & Related papers  (2022-03-07T16:23:47Z)
• FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism [49.89268018642999]
  We propose a fast shape-based network (FS-Net) with efficient category-level feature extraction for 6D pose estimation. The proposed method achieves state-of-the-art performance in both category- and instance-level 6D object pose estimation.
  arXiv  Detail & Related papers  (2021-03-12T03:07:24Z)
• Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation [87.54570024320354]
  State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. We develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds.
  arXiv  Detail & Related papers  (2020-08-04T13:56:19Z)
• DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes [54.239416488865565]
  We propose a fast single-stage 3D object detection method for LIDAR data. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. We find that our proposed method achieves state-of-the-art results by 5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Open dataset.
  arXiv  Detail & Related papers  (2020-04-02T17:48:50Z)
• SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation [3.1542695050861544]
  Estimating 3D orientation and translation of objects is essential for infrastructure-less autonomous navigation and driving. We propose a novel 3D object detection method, named SMOKE, that combines a single keypoint estimate with regressed 3D variables. Despite of its structural simplicity, our proposed SMOKE network outperforms all existing monocular 3D detection methods on the KITTI dataset.
  arXiv  Detail & Related papers  (2020-02-24T08:15:36Z)
• L6DNet: Light 6 DoF Network for Robust and Precise Object Pose Estimation with Small Datasets [0.0]
  We propose a novel approach to perform 6 DoF object pose estimation from a single RGB-D image. We adopt a hybrid pipeline in two stages: data-driven and geometric. Our approach is more robust and accurate than state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-03T17:41:29Z)

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.