Long-Tailed 3D Detection via 2D Late Fusion

• URL: http://arxiv.org/abs/2312.10986v3
• Date: Fri, 14 Jun 2024 14:26:31 GMT
• Title: Long-Tailed 3D Detection via 2D Late Fusion
• Authors: Yechi Ma, Neehar Peri, Shuoquan Wei, Wei Hua, Deva Ramanan, Yanan Li, Shu Kong,
• Abstract summary: Long-Tailed 3D Object Detection (LT3D) addresses the problem of accurately detecting objects from both common and rare classes. We investigate whether to train 2D or 3D RGB detectors, whether to match RGB and LiDAR detections in 3D or the projected 2D image plane for fusion, and how to fuse matched detections.
• Score: 45.75160615737416
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-Tailed 3D Object Detection (LT3D) addresses the problem of accurately detecting objects from both common and rare classes. Contemporary multi-modal detectors achieve low AP on rare-classes (e.g., CMT only achieves 9.4 AP on stroller), presumably because training detectors end-to-end with significant class imbalance is challenging. To address this limitation, we delve into a simple late-fusion framework that ensembles independently trained uni-modal LiDAR and RGB detectors. Importantly, such a late-fusion framework allows us to leverage large-scale uni-modal datasets (with more examples for rare classes) to train better uni-modal RGB detectors, unlike prevailing multimodal detectors that require paired multi-modal training data. Notably, our approach significantly improves rare-class detection by 7.2% over prior work. Further, we examine three critical components of our simple late-fusion approach from first principles and investigate whether to train 2D or 3D RGB detectors, whether to match RGB and LiDAR detections in 3D or the projected 2D image plane for fusion, and how to fuse matched detections. Extensive experiments reveal that 2D RGB detectors achieve better recognition accuracy for rare classes than 3D RGB detectors and matching on the 2D image plane mitigates depth estimation errors. Our late-fusion approach achieves 51.4 mAP on the established nuScenes LT3D benchmark, improving over prior work by 5.9 mAP!

Related papers

• Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection [53.2590751089607]
  Real-IAD D3 is a high-precision multimodal dataset that incorporates an additional pseudo3D modality generated through photometric stereo. We introduce an effective approach that integrates RGB, point cloud, and pseudo-3D depth information to leverage the complementary strengths of each modality. Our experiments highlight the importance of these modalities in boosting detection robustness and overall IAD performance.
  arXiv  Detail & Related papers  (2025-04-19T08:05:47Z)
• 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)
• ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object Detection [15.204935788297226]
  ODM3D framework entails cross-modal knowledge distillation at various levels to inject LiDAR-domain knowledge into a monocular detector during training. By identifying foreground sparsity as the main culprit behind existing methods' suboptimal training, we exploit the precise localisation information embedded in LiDAR points. Our method ranks 1st in both KITTI validation and test benchmarks, significantly surpassing all existing monocular methods, supervised or semi-supervised.
  arXiv  Detail & Related papers  (2023-10-28T07:12:09Z)
• Fully Sparse Fusion for 3D Object Detection [69.32694845027927]
  Currently prevalent multimodal 3D detection methods are built upon LiDAR-based detectors that usually use dense Bird's-Eye-View feature maps. Fully sparse architecture is gaining attention as they are highly efficient in long-range perception. In this paper, we study how to effectively leverage image modality in the emerging fully sparse architecture.
  arXiv  Detail & Related papers  (2023-04-24T17:57:43Z)
• Multimodal Industrial Anomaly Detection via Hybrid Fusion [59.16333340582885]
  We propose a novel multimodal anomaly detection method with hybrid fusion scheme. Our model outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTecD-3 AD dataset.
  arXiv  Detail & Related papers  (2023-03-01T15:48:27Z)
• Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving [91.39625612027386]
  We propose a novel task, called generalized few-shot 3D object detection, where we have a large amount of training data for common (base) objects, but only a few data for rare (novel) classes. Specifically, we analyze in-depth differences between images and point clouds, and then present a practical principle for the few-shot setting in the 3D LiDAR dataset. To solve this task, we propose an incremental fine-tuning method to extend existing 3D detection models to recognize both common and rare objects.
  arXiv  Detail & Related papers  (2023-02-08T07:11:36Z)
• Towards Long-Tailed 3D Detection [56.82185415482943]
  We study the problem of Long-Tailed 3D Detection (LT3D), which evaluates on all classes, including those in-the-tail. Our modifications improve accuracy by 5% AP on average for all classes, and dramatically improve AP for rare classes.
  arXiv  Detail & Related papers  (2022-11-16T06:00:47Z)
• DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection [29.722784254501768]
  DetMatch is a flexible framework for joint semi-supervised learning on 2D and 3D modalities. By identifying objects detected in both sensors, our pipeline generates a cleaner, more robust set of pseudo-labels. We leverage the richer semantics of RGB images to rectify incorrect 3D class predictions and improve localization of 3D boxes.
  arXiv  Detail & Related papers  (2022-03-17T17:58:00Z)
• Dense Voxel Fusion for 3D Object Detection [10.717415797194896]
  Voxel Fusion (DVF) is a sequential fusion method that generates multi-scale dense voxel feature representations. We train directly with ground truth 2D bounding box labels, avoiding noisy, detector-specific, 2D predictions. We show that our proposed multi-modal training strategy results in better generalization compared to training using erroneous 2D predictions.
  arXiv  Detail & Related papers  (2022-03-02T04:51:31Z)
• CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection [13.986963122264633]
  We propose a novel Camera-LiDAR Object Candidates (CLOCs) fusion network. CLOCs fusion provides a low-complexity multi-modal fusion framework. We show that CLOCs ranks the highest among all the fusion-based methods in the official KITTI leaderboard.
  arXiv  Detail & Related papers  (2020-09-02T02:07:00Z)

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.