Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection

• URL: http://arxiv.org/abs/2411.02747v1
• Date: Tue, 05 Nov 2024 02:33:25 GMT
• Title: Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection
• Authors: Yifan Wang, Xiaochen Yang, Fanqi Pu, Qingmin Liao, Wenming Yang,
• Abstract summary: MonoASRH is a novel monocular 3D detection framework composed of Efficient Hybrid Feature Aggregation Module (EH-FAM) and Adaptive Scale-Aware 3D Regression Head (ASRH) EH-FAM employs multi-head attention with a global receptive field to extract semantic features for small-scale objects. ASRH encodes 2D bounding box dimensions and then fuses scale features with the semantic features aggregated by EH-FAM.
• Score: 40.14197775884804
• License:
• Abstract: Monocular 3D object detection has attracted great attention due to simplicity and low cost. Existing methods typically follow conventional 2D detection paradigms, first locating object centers and then predicting 3D attributes via neighboring features. However, these methods predominantly rely on progressive cross-scale feature aggregation and focus solely on local information, which may result in a lack of global awareness and the omission of small-scale objects. In addition, due to large variation in object scales across different scenes and depths, inaccurate receptive fields often lead to background noise and degraded feature representation. To address these issues, we introduces MonoASRH, a novel monocular 3D detection framework composed of Efficient Hybrid Feature Aggregation Module (EH-FAM) and Adaptive Scale-Aware 3D Regression Head (ASRH). Specifically, EH-FAM employs multi-head attention with a global receptive field to extract semantic features for small-scale objects and leverages lightweight convolutional modules to efficiently aggregate visual features across different scales. The ASRH encodes 2D bounding box dimensions and then fuses scale features with the semantic features aggregated by EH-FAM through a scale-semantic feature fusion module. The scale-semantic feature fusion module guides ASRH in learning dynamic receptive field offsets, incorporating scale priors into 3D position prediction for better scale-awareness. Extensive experiments on the KITTI and Waymo datasets demonstrate that MonoASRH achieves state-of-the-art performance.

Related papers

• MonoMM: A Multi-scale Mamba-Enhanced Network for Real-time Monocular 3D Object Detection [9.780498146964097]
  We propose an innovative network architecture, MonoMM, for real-time monocular 3D object detection. MonoMM consists of Focused Multi-Scale Fusion (FMF) and Depth-Aware Feature Enhancement Mamba (DMB) modules. Our method outperforms previous monocular methods and achieves real-time detection.
  arXiv  Detail & Related papers  (2024-08-01T10:16:58Z)
• S$^3$-MonoDETR: Supervised Shape&Scale-perceptive Deformable Transformer for Monocular 3D Object Detection [21.96072831561483]
  This paper proposes a novel Supervised Shape&Scale-perceptive Deformable Attention'' (S$3$-DA) module for monocular 3D object detection. Benefiting from this, S$3$-DA effectively estimates receptive fields for query points belonging to any category, enabling them to generate robust query features. Experiments on KITTI and Open datasets demonstrate that S$3$-DA significantly improves the detection accuracy.
  arXiv  Detail & Related papers  (2023-09-02T12:36:38Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection [62.1185839286255]
  Low-cost monocular 3D object detection plays a fundamental role in autonomous driving. We introduce a Dynamic Feature Reflecting Network, named DFR-Net. We rank 1st among all the monocular 3D object detectors in the KITTI test set.
  arXiv  Detail & Related papers  (2021-12-28T07:31:18Z)
• SGM3D: Stereo Guided Monocular 3D Object Detection [62.11858392862551]
  We propose a stereo-guided monocular 3D object detection network, termed SGM3D. We exploit robust 3D features extracted from stereo images to enhance the features learned from the monocular image. Our method can be integrated into many other monocular approaches to boost performance without introducing any extra computational cost.
  arXiv  Detail & Related papers  (2021-12-03T13:57:14Z)
• LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition [38.540048855119004]
  We propose a novel Locality-Aware Point-View Fusion Transformer (LATFormer) for 3D shape retrieval and classification. The core component of LATFormer is a module named Locality-Aware Fusion (LAF) which integrates the local features of correlated regions across the two modalities. In our LATFormer, we utilize the LAF module to fuse the multi-scale features of the two modalities both bidirectionally and hierarchically to obtain more informative features.
  arXiv  Detail & Related papers  (2021-09-03T03:23:27Z)
• M3DSSD: Monocular 3D Single Stage Object Detector [82.25793227026443]
  We propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. The proposed M3DSSD achieves significantly better performance than the monocular 3D object detection methods on the KITTI dataset.
  arXiv  Detail & Related papers  (2021-03-24T13:09:11Z)
• IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image [37.83574424518901]
  3D object detection from a single image is an important task in Autonomous Driving. We propose an instance-aware approach to aggregate useful information for improving the accuracy of 3D object detection.
  arXiv  Detail & Related papers  (2021-03-05T05:47:52Z)
• Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
  arXiv  Detail & Related papers  (2020-08-31T17:10:48Z)

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.