Weakly Supervised 3D Object Detection via Multi-Level Visual Guidance

• URL: http://arxiv.org/abs/2312.07530v3
• Date: Tue, 20 Aug 2024 19:12:31 GMT
• Title: Weakly Supervised 3D Object Detection via Multi-Level Visual Guidance
• Authors: Kuan-Chih Huang, Yi-Hsuan Tsai, Ming-Hsuan Yang,
• Abstract summary: We propose a framework to study how to leverage constraints between 2D and 3D domains without requiring any 3D labels. Specifically, we design a feature-level constraint to align LiDAR and image features based on object-aware regions. Second, the output-level constraint is developed to enforce the overlap between 2D and projected 3D box estimations. Third, the training-level constraint is utilized by producing accurate and consistent 3D pseudo-labels that align with the visual data.
• Score: 72.6809373191638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised 3D object detection aims to learn a 3D detector with lower annotation cost, e.g., 2D labels. Unlike prior work which still relies on few accurate 3D annotations, we propose a framework to study how to leverage constraints between 2D and 3D domains without requiring any 3D labels. Specifically, we employ visual data from three perspectives to establish connections between 2D and 3D domains. First, we design a feature-level constraint to align LiDAR and image features based on object-aware regions. Second, the output-level constraint is developed to enforce the overlap between 2D and projected 3D box estimations. Finally, the training-level constraint is utilized by producing accurate and consistent 3D pseudo-labels that align with the visual data. We conduct extensive experiments on the KITTI dataset to validate the effectiveness of the proposed three constraints. Without using any 3D labels, our method achieves favorable performance against state-of-the-art approaches and is competitive with the method that uses 500-frame 3D annotations. Code will be made publicly available at https://github.com/kuanchihhuang/VG-W3D.

Related papers

• Training an Open-Vocabulary Monocular 3D Object Detection Model without 3D Data [57.53523870705433]
  We propose a novel open-vocabulary monocular 3D object detection framework, dubbed OVM3D-Det. OVM3D-Det does not require high-precision LiDAR or 3D sensor data for either input or generating 3D bounding boxes. It employs open-vocabulary 2D models and pseudo-LiDAR to automatically label 3D objects in RGB images, fostering the learning of open-vocabulary monocular 3D detectors.
  arXiv  Detail & Related papers  (2024-11-23T21:37:21Z)
• ALPI: Auto-Labeller with Proxy Injection for 3D Object Detection using 2D Labels Only [5.699475977818167]
  3D object detection plays a crucial role in various applications such as autonomous vehicles, robotics and augmented reality. We propose a weakly supervised 3D annotator that relies solely on 2D bounding box annotations from images, along with size priors.
  arXiv  Detail & Related papers  (2024-07-24T11:58:31Z)
• General Geometry-aware Weakly Supervised 3D Object Detection [62.26729317523975]
  A unified framework is developed for learning 3D object detectors from RGB images and associated 2D boxes. Experiments on KITTI and SUN-RGBD datasets demonstrate that our method yields surprisingly high-quality 3D bounding boxes with only 2D annotation.
  arXiv  Detail & Related papers  (2024-07-18T17:52:08Z)
• Tracking Objects with 3D Representation from Videos [57.641129788552675]
  We propose a new 2D Multiple Object Tracking paradigm, called P3DTrack. With 3D object representation learning from Pseudo 3D object labels in monocular videos, we propose a new 2D MOT paradigm, called P3DTrack.
  arXiv  Detail & Related papers  (2023-06-08T17:58:45Z)
• Towards 3D Object Detection with 2D Supervision [13.444432119639822]
  We introduce a hybrid training framework, enabling us to learn a visual 3D object detector with massive 2D labels. We propose a temporal 2D transformation to bridge the 3D predictions with temporal 2D labels. Experiments conducted on the nuScenes dataset show strong results (nearly 90% of its fully-supervised performance) with only 25% 3D annotations.
  arXiv  Detail & Related papers  (2022-11-15T16:40:11Z)
• 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)
• FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection [81.79171905308827]
  We propose frustum-aware geometric reasoning (FGR) to detect vehicles in point clouds without any 3D annotations. Our method consists of two stages: coarse 3D segmentation and 3D bounding box estimation. It is able to accurately detect objects in 3D space with only 2D bounding boxes and sparse point clouds.
  arXiv  Detail & Related papers  (2021-05-17T07:29:55Z)
• RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving [26.216609821525676]
  Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. Our method is the first real-time system for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark.
  arXiv  Detail & Related papers  (2020-01-10T08:29:20Z)

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.