Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training

• URL: http://arxiv.org/abs/2310.00920v4
• Date: Tue, 24 Sep 2024 10:18:44 GMT
• Title: Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training
• Authors: Fulong Ma, Xiaoyang Yan, Guoyang Zhao, Xiaojie Xu, Yuxuan Liu, Jun Ma, Ming Liu,
• Abstract summary: This study investigates the pipeline for training a monocular 3D object detection model on a diverse collection of 3D and 2D datasets. The proposed framework comprises three components: (1) a robust monocular 3D model capable of functioning across various camera settings, (2) a selective-training strategy to accommodate datasets with differing class annotations, and (3) a pseudo 3D training approach using 2D labels to enhance detection performance in scenes containing only 2D labels.
• Score: 9.272389295055271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monocular 3D object detection plays a crucial role in autonomous driving. However, existing monocular 3D detection algorithms depend on 3D labels derived from LiDAR measurements, which are costly to acquire for new datasets and challenging to deploy in novel environments. Specifically, this study investigates the pipeline for training a monocular 3D object detection model on a diverse collection of 3D and 2D datasets. The proposed framework comprises three components: (1) a robust monocular 3D model capable of functioning across various camera settings, (2) a selective-training strategy to accommodate datasets with differing class annotations, and (3) a pseudo 3D training approach using 2D labels to enhance detection performance in scenes containing only 2D labels. With this framework, we could train models on a joint set of various open 3D/2D datasets to obtain models with significantly stronger generalization capability and enhanced performance on new dataset with only 2D labels. We conduct extensive experiments on KITTI/nuScenes/ONCE/Cityscapes/BDD100K datasets to demonstrate the scaling ability of the proposed method.

Related papers

• Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene [22.297964850282177]
  We propose LiDAR-2D Self-paced Learning (LiSe) for unsupervised 3D detection. RGB images serve as a valuable complement to LiDAR data, offering precise 2D localization cues. Our framework devises a self-paced learning pipeline that incorporates adaptive sampling and weak model aggregation strategies.
  arXiv  Detail & Related papers  (2024-07-11T14:58:49Z)
• Cross-Modal Self-Training: Aligning Images and Pointclouds to Learn Classification without Labels [69.55622471172941]
  Large-scale vision 2D vision language models, such as CLIP can be aligned with a 3D encoder to learn generalizable (open-vocabulary) 3D vision models. We propose an optimization framework: Cross-MoST: Cross-Modal Self-Training, to improve the label-free classification performance of a zero-shot 3D vision model.
  arXiv  Detail & Related papers  (2024-04-15T21:30:50Z)
• VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection [80.62052650370416]
  monocular 3D object detection holds significant importance across various applications, including autonomous driving and robotics. In this paper, we present VFMM3D, an innovative framework that leverages the capabilities of Vision Foundation Models (VFMs) to accurately transform single-view images into LiDAR point cloud representations.
  arXiv  Detail & Related papers  (2024-04-15T03:12:12Z)
• OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation [67.56268991234371]
  OV-Uni3DETR achieves the state-of-the-art performance on various scenarios, surpassing existing methods by more than 6% on average. Code and pre-trained models will be released later.
  arXiv  Detail & Related papers  (2024-03-28T17:05:04Z)
• Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation [67.07112533415116]
  We present a novel framework that adapts various foundational models for the 3D point cloud segmentation task. Our approach involves making initial predictions of 2D semantic masks using different large vision models. To generate robust 3D semantic pseudo labels, we introduce a semantic label fusion strategy that effectively combines all the results via voting.
  arXiv  Detail & Related papers  (2023-11-03T15:41:15Z)
• MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection Generalization [3.8243923744440926]
  3D object detection models trained on a source dataset with a specific point distribution have shown difficulties in generalizing to unseen datasets. We leverage the information available from several annotated source datasets with our Multi-Dataset Training for 3D Object Detection (MDT3D) method. We show how we managed the mix of datasets during training and finally introduce a new cross-dataset augmentation method: cross-dataset object injection.
  arXiv  Detail & Related papers  (2023-08-02T08:20:00Z)
• 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)
• Monocular Quasi-Dense 3D Object Tracking [99.51683944057191]
  A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform.
  arXiv  Detail & Related papers  (2021-03-12T15:30:02Z)
• 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)

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.