Learning Class Prototypes for Unified Sparse Supervised 3D Object Detection

• URL: http://arxiv.org/abs/2503.21099v1
• Date: Thu, 27 Mar 2025 02:37:05 GMT
• Title: Learning Class Prototypes for Unified Sparse Supervised 3D Object Detection
• Authors: Yun Zhu, Le Hui, Hang Yang, Jianjun Qian, Jin Xie, Jian Yang,
• Abstract summary: We propose a unified sparse supervised 3D object detection method for both indoor and outdoor scenes.<n>Our method achieves about 78%, 90%, and 96% performance compared to the fully supervised detector on ScanNet V2, SUN RGB-D, and KITTI.
• Score: 36.851528695465475
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Both indoor and outdoor scene perceptions are essential for embodied intelligence. However, current sparse supervised 3D object detection methods focus solely on outdoor scenes without considering indoor settings. To this end, we propose a unified sparse supervised 3D object detection method for both indoor and outdoor scenes through learning class prototypes to effectively utilize unlabeled objects. Specifically, we first propose a prototype-based object mining module that converts the unlabeled object mining into a matching problem between class prototypes and unlabeled features. By using optimal transport matching results, we assign prototype labels to high-confidence features, thereby achieving the mining of unlabeled objects. We then present a multi-label cooperative refinement module to effectively recover missed detections through pseudo label quality control and prototype label cooperation. Experiments show that our method achieves state-of-the-art performance under the one object per scene sparse supervised setting across indoor and outdoor datasets. With only one labeled object per scene, our method achieves about 78%, 90%, and 96% performance compared to the fully supervised detector on ScanNet V2, SUN RGB-D, and KITTI, respectively, highlighting the scalability of our method. Code is available at https://github.com/zyrant/CPDet3D.

Related papers

• GrabS: Generative Embodied Agent for 3D Object Segmentation without Scene Supervision [7.511342491529451]
  We study the hard problem of 3D object segmentation in complex point clouds without requiring human labels of 3D scenes for supervision. By relying on the similarity of pretrained 2D features or external signals such as motion to group 3D points as objects, existing unsupervised methods are usually limited to identifying simple objects like cars or their segmented objects are often inferior due to the lack of objectness in pretrained features.
  arXiv  Detail & Related papers  (2025-04-16T04:13:53Z)
• Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels [40.571133087275406]
  Multi-agent collaborative dataset, which involves the sharing of complementary observations among agents, holds the potential to break through this bottleneck.<n>We introduce a novel unsupervised method that learns to Detect Objects from Multi-Agent LiDAR scans, termed DOtA, without using labels from external.<n>DOtA uses the complementary observations between agents to perform multi-scale encoding on preliminary labels, then decodes high-quality and low-quality labels.
  arXiv  Detail & Related papers  (2025-03-11T13:34:35Z)
• Dual-Perspective Knowledge Enrichment for Semi-Supervised 3D Object Detection [55.210991151015534]
  We present a novel Dual-Perspective Knowledge Enrichment approach named DPKE for semi-supervised 3D object detection. Our DPKE enriches the knowledge of limited training data, particularly unlabeled data, from two perspectives: data-perspective and feature-perspective.
  arXiv  Detail & Related papers  (2024-01-10T08:56:07Z)
• PatchContrast: Self-Supervised Pre-training for 3D Object Detection [14.603858163158625]
  We introduce PatchContrast, a novel self-supervised point cloud pre-training framework for 3D object detection. We show that our method outperforms existing state-of-the-art models on three commonly-used 3D detection datasets.
  arXiv  Detail & Related papers  (2023-08-14T07:45:54Z)
• Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection [50.959453059206446]
  This paper aims for high-performance offline LiDAR-based 3D object detection. We first observe that experienced human annotators annotate objects from a track-centric perspective. We propose a high-performance offline detector in a track-centric perspective instead of the conventional object-centric perspective.
  arXiv  Detail & Related papers  (2023-04-24T17:59:05Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• Semi-supervised 3D Object Detection via Adaptive Pseudo-Labeling [18.209409027211404]
  3D object detection is an important task in computer vision. Most existing methods require a large number of high-quality 3D annotations, which are expensive to collect. We propose a novel semi-supervised framework based on pseudo-labeling for outdoor 3D object detection tasks.
  arXiv  Detail & Related papers  (2021-08-15T02:58:43Z)
• 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection [76.42897462051067]
  3DIoUMatch is a novel semi-supervised method for 3D object detection applicable to both indoor and outdoor scenes. We leverage a teacher-student mutual learning framework to propagate information from the labeled to the unlabeled train set in the form of pseudo-labels. Our method consistently improves state-of-the-art methods on both ScanNet and SUN-RGBD benchmarks by significant margins under all label ratios.
  arXiv  Detail & Related papers  (2020-12-08T11:06:26Z)
• Move to See Better: Self-Improving Embodied Object Detection [35.461141354989714]
  We propose a method for improving object detection in testing environments. Our agent collects multi-view data, generates 2D and 3D pseudo-labels, and fine-tunes its detector in a self-supervised manner.
  arXiv  Detail & Related papers  (2020-11-30T19:16:51Z)
• Unsupervised Object Detection with LiDAR Clues [70.73881791310495]
  We present the first practical method for unsupervised object detection with the aid of LiDAR clues. In our approach, candidate object segments based on 3D point clouds are firstly generated. Then, an iterative segment labeling process is conducted to assign segment labels and to train a segment labeling network. The labeling process is carefully designed so as to mitigate the issue of long-tailed and open-ended distribution.
  arXiv  Detail & Related papers  (2020-11-25T18:59:54Z)
• SESS: Self-Ensembling Semi-Supervised 3D Object Detection [138.80825169240302]
  We propose SESS, a self-ensembling semi-supervised 3D object detection framework. Specifically, we design a thorough perturbation scheme to enhance generalization of the network on unlabeled and new unseen data. Our SESS achieves competitive performance compared to the state-of-the-art fully-supervised method by using only 50% labeled data.
  arXiv  Detail & Related papers  (2019-12-26T08:48:04Z)

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.