Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration

• URL: http://arxiv.org/abs/2401.12452v3
• Date: Wed, 16 Oct 2024 14:19:28 GMT
• Title: Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
• Authors: Yifan Zhang, Siyu Ren, Junhui Hou, Jinjian Wu, Yixuan Yuan, Guangming Shi,
• Abstract summary: This paper introduces a novel self-supervised learning framework for enhancing 3D perception in autonomous driving scenes. We propose the learnable transformation alignment to bridge the domain gap between image and point cloud data. We establish dense 2D-3D correspondences to estimate the rigid pose.
• Score: 107.61458720202984
• License:
• Abstract: This paper introduces a novel self-supervised learning framework for enhancing 3D perception in autonomous driving scenes. Specifically, our approach, namely NCLR, focuses on 2D-3D neural calibration, a novel pretext task that estimates the rigid pose aligning camera and LiDAR coordinate systems. First, we propose the learnable transformation alignment to bridge the domain gap between image and point cloud data, converting features into a unified representation space for effective comparison and matching. Second, we identify the overlapping area between the image and point cloud with the fused features. Third, we establish dense 2D-3D correspondences to estimate the rigid pose. The framework not only learns fine-grained matching from points to pixels but also achieves alignment of the image and point cloud at a holistic level, understanding their relative pose. We demonstrate the efficacy of NCLR by applying the pre-trained backbone to downstream tasks, such as LiDAR-based 3D semantic segmentation, object detection, and panoptic segmentation. Comprehensive experiments on various datasets illustrate the superiority of NCLR over existing self-supervised methods. The results confirm that joint learning from different modalities significantly enhances the network's understanding abilities and effectiveness of learned representation. The code is publicly available at https://github.com/Eaphan/NCLR.

Related papers

• HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation [106.09886920774002]
  We present a hybrid-view-based knowledge distillation framework, termed HVDistill, to guide the feature learning of a point cloud neural network. Our method achieves consistent improvements over the baseline trained from scratch and significantly out- performs the existing schemes.
  arXiv  Detail & Related papers  (2024-03-18T14:18:08Z)
• Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration [17.420425069785946]
  We present a novel Cross-Modal Information-Guided Network (CMIGNet) for point cloud registration. We first incorporate the projected images from the point clouds and fuse the cross-modal features using the attention mechanism. We employ two contrastive learning strategies, namely overlapping contrastive learning and cross-modal contrastive learning.
  arXiv  Detail & Related papers  (2023-11-02T12:56:47Z)
• Unleash the Potential of Image Branch for Cross-modal 3D Object Detection [67.94357336206136]
  We present a new cross-modal 3D object detector, namely UPIDet, which aims to unleash the potential of the image branch from two aspects. First, UPIDet introduces a new 2D auxiliary task called normalized local coordinate map estimation. Second, we discover that the representational capability of the point cloud backbone can be enhanced through the gradients backpropagated from the training objectives of the image branch.
  arXiv  Detail & Related papers  (2023-01-22T08:26:58Z)
• Visual Reinforcement Learning with Self-Supervised 3D Representations [15.991546692872841]
  We present a unified framework for self-supervised learning of 3D representations for motor control. Our method enjoys improved sample efficiency in simulated manipulation tasks compared to 2D representation learning methods.
  arXiv  Detail & Related papers  (2022-10-13T17:59:55Z)
• Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data [80.14669385741202]
  We propose a self-supervised pre-training method for 3D perception models tailored to autonomous driving data. We leverage the availability of synchronized and calibrated image and Lidar sensors in autonomous driving setups. Our method does not require any point cloud nor image annotations.
  arXiv  Detail & Related papers  (2022-03-30T12:40:30Z)
• CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding [2.8661021832561757]
  CrossPoint is a simple cross-modal contrastive learning approach to learn transferable 3D point cloud representations. Our approach outperforms the previous unsupervised learning methods on a diverse range of downstream tasks including 3D object classification and segmentation.
  arXiv  Detail & Related papers  (2022-03-01T18:59:01Z)
• SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations [85.38562724999898]
  We propose a 2D Image and 3D Point cloud Unsupervised pre-training strategy, called SimIPU. Specifically, we develop a multi-modal contrastive learning framework that consists of an intra-modal spatial perception module and an inter-modal feature interaction module. To the best of our knowledge, this is the first study to explore contrastive learning pre-training strategies for outdoor multi-modal datasets.
  arXiv  Detail & Related papers  (2021-12-09T03:27:00Z)
• Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences [32.01548991331616]
  This paper presents a novel self-supervised learning approach to learn both 2D image features and 3D point cloud features. It exploits cross-modality and cross-view correspondences without using any annotated human labels. The effectiveness of the learned 2D and 3D features is evaluated by transferring them on five different tasks.
  arXiv  Detail & Related papers  (2020-04-13T02:57:25Z)
• Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem [98.92148855291363]
  This paper proposes a deep CNN model which simultaneously solves for both 6-DoF absolute camera pose 2D--3D correspondences. Tests on both real and simulated data have shown that our method substantially outperforms existing approaches.
  arXiv  Detail & Related papers  (2020-03-15T04:17:30Z)

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.