PSA-Det3D: Pillar Set Abstraction for 3D object Detection
- URL: http://arxiv.org/abs/2210.10983v1
- Date: Thu, 20 Oct 2022 03:05:34 GMT
- Title: PSA-Det3D: Pillar Set Abstraction for 3D object Detection
- Authors: Zhicong Huang, Jingwen Zhao, Zhijie Zheng, Dihu Chena, Haifeng Hu
- Abstract summary: We propose a pillar set abstraction (PSA) and foreground point compensation (FPC) to improve the detection performance for small object.
The experiments on the KITTI 3D detection benchmark show that our proposed PSA-Det3D outperforms other algorithms with high accuracy for small object detection.
- Score: 14.788139868324155
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Small object detection for 3D point cloud is a challenging problem because of
two limitations: (1) Perceiving small objects is much more diffcult than normal
objects due to the lack of valid points. (2) Small objects are easily blocked
which breaks the shape of their meshes in 3D point cloud. In this paper, we
propose a pillar set abstraction (PSA) and foreground point compensation (FPC)
and design a point-based detection network, PSA-Det3D, to improve the detection
performance for small object. The PSA embeds a pillar query operation on the
basis of set abstraction (SA) to expand its receptive field of the network,
which can aggregate point-wise features effectively. To locate more occluded
objects, we persent a proposal generation layer consisting of a foreground
point segmentation and a FPC module. Both the foreground points and the
estimated centers are finally fused together to generate the detection result.
The experiments on the KITTI 3D detection benchmark show that our proposed
PSA-Det3D outperforms other algorithms with high accuracy for small object
detection.
Related papers
- OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection [102.0744303467713]
We propose a new multi-view 3D object detector named OPEN.
Our main idea is to effectively inject object-wise depth information into the network through our proposed object-wise position embedding.
OPEN achieves a new state-of-the-art performance with 64.4% NDS and 56.7% mAP on the nuScenes test benchmark.
arXiv Detail & Related papers (2024-07-15T14:29:15Z) - 3D Small Object Detection with Dynamic Spatial Pruning [62.72638845817799]
We propose an efficient feature pruning strategy for 3D small object detection.
We present a multi-level 3D detector named DSPDet3D which benefits from high spatial resolution.
It takes less than 2s to directly process a whole building consisting of more than 4500k points while detecting out almost all objects.
arXiv Detail & Related papers (2023-05-05T17:57:04Z) - RBGNet: Ray-based Grouping for 3D Object Detection [104.98776095895641]
We propose the RBGNet framework, a voting-based 3D detector for accurate 3D object detection from point clouds.
We propose a ray-based feature grouping module, which aggregates the point-wise features on object surfaces using a group of determined rays.
Our model achieves state-of-the-art 3D detection performance on ScanNet V2 and SUN RGB-D with remarkable performance gains.
arXiv Detail & Related papers (2022-04-05T14:42:57Z) - SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object
Detection [78.90102636266276]
We propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA)
Based on the estimated point-wise foreground scores, we then propose a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling.
In practice, SASA shows to be effective in identifying valuable points related to foreground objects and improving feature learning for point-based 3D detection.
arXiv Detail & Related papers (2022-01-06T08:54:47Z) - Group-Free 3D Object Detection via Transformers [26.040378025818416]
We present a simple yet effective method for directly detecting 3D objects from the 3D point cloud.
Our method computes the feature of an object from all the points in the point cloud with the help of an attention mechanism in the Transformers citevaswaniattention.
With few bells and whistles, the proposed method achieves state-of-the-art 3D object detection performance on two widely used benchmarks, ScanNet V2 and SUN RGB-D.
arXiv Detail & Related papers (2021-04-01T17:59:36Z) - Delving into Localization Errors for Monocular 3D Object Detection [85.77319416168362]
Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving.
In this work, we quantify the impact introduced by each sub-task and find the localization error' is the vital factor in restricting monocular 3D detection.
arXiv Detail & Related papers (2021-03-30T10:38:01Z) - SIENet: Spatial Information Enhancement Network for 3D Object Detection
from Point Cloud [20.84329063509459]
LiDAR-based 3D object detection pushes forward an immense influence on autonomous vehicles.
Due to the limitation of the intrinsic properties of LiDAR, fewer points are collected at the objects farther away from the sensor.
To address the challenge, we propose a novel two-stage 3D object detection framework, named SIENet.
arXiv Detail & Related papers (2021-03-29T07:45:09Z) - 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) - Object as Hotspots: An Anchor-Free 3D Object Detection Approach via
Firing of Hotspots [37.16690737208046]
We argue for an approach opposite to existing methods using object-level anchors.
Inspired by compositional models, we propose an object as composition of its interior non-empty voxels, termed hotspots.
Based on OHS, we propose an anchor-free detection head with a novel ground truth assignment strategy.
arXiv Detail & Related papers (2019-12-30T03:02:22Z)
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.