SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining Environments: A Novel Dataset and ROS-Based Automatic Annotation Pipeline

• URL: http://arxiv.org/abs/2312.06113v1
• Date: Mon, 11 Dec 2023 04:33:45 GMT
• Title: SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining Environments: A Novel Dataset and ROS-Based Automatic Annotation Pipeline
• Authors: Mehala Balamurali and Ehsan Mihankhah
• Abstract summary: We introduce a synthetic dataset SimMining 3D specifically designed for 3D object detection in mining environments. The dataset captures objects and sensors positioned at various heights within mine benches, accurately reflecting authentic mining scenarios. We propose evaluation metrics accounting for sensor-to-object height variations and point cloud density, enabling accurate model assessment.
• Score: 0.9790236766474201
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate and efficient object detection is crucial for safe and efficient operation of earth-moving equipment in mining. Traditional 2D image-based methods face limitations in dynamic and complex mine environments. To overcome these challenges, 3D object detection using point cloud data has emerged as a comprehensive approach. However, training models for mining scenarios is challenging due to sensor height variations, viewpoint changes, and the need for diverse annotated datasets. This paper presents novel contributions to address these challenges. We introduce a synthetic dataset SimMining 3D [1] specifically designed for 3D object detection in mining environments. The dataset captures objects and sensors positioned at various heights within mine benches, accurately reflecting authentic mining scenarios. An automatic annotation pipeline through ROS interface reduces manual labor and accelerates dataset creation. We propose evaluation metrics accounting for sensor-to-object height variations and point cloud density, enabling accurate model assessment in mining scenarios. Real data tests validate our models effectiveness in object prediction. Our ablation study emphasizes the importance of altitude and height variation augmentations in improving accuracy and reliability. The publicly accessible synthetic dataset [1] serves as a benchmark for supervised learning and advances object detection techniques in mining with complimentary pointwise annotations for each scene. In conclusion, our work bridges the gap between synthetic and real data, addressing the domain shift challenge in 3D object detection for mining. We envision robust object detection systems enhancing safety and efficiency in mining and related domains.

Related papers

• Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection [59.33188668341604]
  3D object detection serves as the fundamental task of autonomous driving perception. It is costly to obtain high-quality annotations for point cloud data. We propose a hardness-aware scene synthesis (HASS) method to generate adaptive synthetic scenes.
  arXiv  Detail & Related papers  (2024-05-27T17:59:23Z)
• Label-Efficient 3D Object Detection For Road-Side Units [10.663986706501188]
  Collaborative perception can enhance the perception of autonomous vehicles via deep information fusion with intelligent roadside units (RSU) The data-hungry nature of these methods creates a major hurdle for their real-world deployment, particularly due to the need for annotated RSU data. We devise a label-efficient object detection method for RSU based on unsupervised object discovery.
  arXiv  Detail & Related papers  (2024-04-09T12:29:16Z)
• Multi-Modal Dataset Acquisition for Photometrically Challenging Object [56.30027922063559]
  This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects. We propose a novel annotation and acquisition pipeline that enhances existing 3D perception and 6D object pose datasets.
  arXiv  Detail & Related papers  (2023-08-21T10:38:32Z)
• On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks [61.74608497496841]
  Training on inaccurate or corrupt data induces model bias and hampers generalisation capabilities. This paper investigates the effect of sensor errors for the dense 3D vision tasks of depth estimation and reconstruction.
  arXiv  Detail & Related papers  (2023-03-26T22:32:44Z)
• Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents [49.904531485843464]
  In this paper, we discuss the main challenge: insufficient, or even no, labeled data for real-world indoor environments. We describe MMISM (Multi-modality input Multi-task output Indoor Scene understanding Model) to tackle the above challenges. MMISM considers RGB images as well as sparse Lidar points as inputs and 3D object detection, depth completion, human pose estimation, and semantic segmentation as output tasks. We show that MMISM performs on par or even better than single-task models.
  arXiv  Detail & Related papers  (2022-09-27T04:49:19Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• Neural-Sim: Learning to Generate Training Data with NeRF [31.81496344354997]
  We present the first fully differentiable synthetic data pipeline that uses Neural Radiance Fields (NeRFs) in a closed-loop with a target application's loss function. Our approach generates data on-demand, with no human labor, to maximize accuracy for a target task.
  arXiv  Detail & Related papers  (2022-07-22T22:48:33Z)
• Object DGCNN: 3D Object Detection using Dynamic Graphs [32.090268859180334]
  3D object detection often involves complicated training and testing pipelines. Inspired by recent non-maximum suppression-free 2D object detection models, we propose a 3D object detection architecture on point clouds.
  arXiv  Detail & Related papers  (2021-10-13T17:59:38Z)
• Ground material classification and for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach [1.3359609092684614]
  In recent years, photogrammetry has been widely used in many areas to create 3D virtual data representing the physical environment. These cutting-edge technologies have caught the US Army and Navy's attention for the purpose of rapid 3D battlefield reconstruction, virtual training, and simulations.
  arXiv  Detail & Related papers  (2021-09-24T22:29:26Z)
• Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
  arXiv  Detail & Related papers  (2020-08-31T17:10:48Z)

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.