PillarNeXt: Rethinking network designs for 3D object detection in LiDAR point clouds

J Li, C Luo, X Yang - … of the IEEE/CVF Conference on …, 2023 - openaccess.thecvf.com
In order to deal with the sparse and unstructured raw point clouds, most LiDAR based 3D
object detection research focuses on designing dedicated local point aggregators for fine …
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PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds

J Li, C Luo, X Yang - openaccess.thecvf.com
4, π 4], random scaling between [0.9, 1.1], and random translation with a noise factor of 0.5,
which are used for all experiments. Table 9 shows the detailed architecture and training …

PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds

J Li, C Luo, X Yang - arXiv preprint arXiv:2305.04925, 2023 - arxiv.org
In order to deal with the sparse and unstructured raw point clouds, LiDAR based 3D object
detection research mostly focuses on designing dedicated local point aggregators for fine …

PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds

J Li, C Luo, X Yang - 2023 IEEE/CVF Conference on Computer …, 2023 - ieeexplore.ieee.org
In order to deal with the sparse and unstructured raw point clouds, most LiDAR based 3D
object detection research focuses on designing dedicated local point aggregators for fine …

PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds

J Li, C Luo, X Yang - 2023 IEEE/CVF Conference on Computer Vision …, 2023 - computer.org
In order to deal with the sparse and unstructured raw point clouds, most LiDAR based 3D
object detection research focuses on designing dedicated local point aggregators for fine …

PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds

J Li, C Luo, X Yang - arXiv e-prints, 2023 - ui.adsabs.harvard.edu
In order to deal with the sparse and unstructured raw point clouds, LiDAR based 3D object
detection research mostly focuses on designing dedicated local point aggregators for fine …