MuRF-Net: Multi-Receptive Field Pillars for 3D Object Detection from Point Cloud

Yang, Zhuo; Huang, Yuhao; Yan, Xinrui; Chen, Shitao; Dong, Jinpeng; Sun, Jun; Liu, Ziyi; Nan, Zhixiong; Zheng, Nanning

doi:10.1109/iv47402.2020.9304629

Cited by 1 publication

(4 citation statements)

References 24 publications

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“…Compared with the voxel-based approach, the pillar-based approach [3], [8], [9], [17], [19], [22] aims to reduce the time consumption during inference. These methods adjust the grid height to be equivalent to the height of the 3D space during the point cloud voxelization, thereby directly transforming the point cloud from the 3D shape to the 2D form in the BEV space.…”

Section: B Pillar-based Methodsmentioning

confidence: 99%

“…Infofocus [10] adds a second-stage attention network to PointPillars [3] for fine-grained proposal refinement. MuRF-Net [9] introduces the utilization of dilated operations in the voxelization process to acquire BEV features with varying receptive fields, followed by channel-wise attention for fusion. CVFNet [18] first projects the point clouds onto the range view to extract point-wise features through a 2D convolutional network and then voxelizes them to obtain BEV features, thereby enabling the 3D detector to capture information from various perspectives.…”

Section: B Pillar-based Methodsmentioning

confidence: 99%

“…Pillar-based PointPillar [3] Pooling Pillar-OD [8] Pooling MuRF-Net [9] Pooling InfoFocus [10] Pooling CenterPoint-pillar [13] Pooling WCNN3D [17] Pooling CVFNet [18] Pooling Pillargrid [19] Pooling PillarNet [22] Pooling…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…The transformation of point clouds into bird's-eye view (BEV) features can cause information loss. To address this issue, some approaches [9], [18] have suggested utilizing a dedicated network for the extraction of features from point clouds before the transformation. Nevertheless, there is no approach that concentrates on the operations employed for transforming 3D voxel or point features into BEV features.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Grid-Based 3D Object Detection in Autonomous Driving With Improved Dimensionality Reduction

et al. 2023

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Point cloud object detection is a pivotal technology in autonomous driving and robotics. Currently, the majority of cutting-edge point cloud detectors utilize Bird's Eye View (BEV) for detection, as it allows them to take advantage of well-explored 2D detection techniques. Nevertheless, dimensionality reduction of features from 3D space to BEV space unavoidably leads to information loss, and there is a lack of research on this issue. Existing methods typically obtain BEV features by collapsing voxel or point features along the height dimension via a pooling operation or convolution, resulting in a significant decrease in geometric information. To tackle this problem, we present a new point cloud backbone network for grid-based object detection, MDRNet, which is based on adaptive dimensionality reduction and multilevel spatial residual strategies. In MDRNet, the Spatial-aware Dimensionality Reduction (SDR) is designed to dynamically concentrate on the essential components of the object during 3D-to-BEV transformation. Moreover, the Multi-level Spatial Residuals (MSR) strategy is proposed to effectively fuse multi-level spatial information in BEV feature maps. Our MDRNet can be employed on any existing grid-based object detector, resulting in a remarkable improvement in performance. Numerous experiments conducted on nuScenes, KITTI and DAIR-V have shown that MDRNet surpasses existing SOTA approaches. In particular, on the nuScenes dataset, we attained an impressive 7.2% mAP and 5.0% NDS enhancement compared with CenterPoint.

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