SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection

• URL: http://arxiv.org/abs/2201.01976v1
• Date: Thu, 6 Jan 2022 08:54:47 GMT
• Title: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection
• Authors: Chen Chen, Zhe Chen, Jing Zhang and Dacheng Tao
• Abstract summary: 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.
• Score: 78.90102636266276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although point-based networks are demonstrated to be accurate for 3D point cloud modeling, they are still falling behind their voxel-based competitors in 3D detection. We observe that the prevailing set abstraction design for down-sampling points may maintain too much unimportant background information that can affect feature learning for detecting objects. To tackle this issue, we propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA). Technically, we first add a binary segmentation module as the side output to help identify foreground points. 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. Additionally, it is an easy-to-plug-in module and able to boost various point-based detectors, including single-stage and two-stage ones. Extensive experiments on the popular KITTI and nuScenes datasets validate the superiority of SASA, lifting point-based detection models to reach comparable performance to state-of-the-art voxel-based methods.

Related papers

• PSA-Det3D: Pillar Set Abstraction for 3D object Detection [14.788139868324155]
  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.
  arXiv  Detail & Related papers  (2022-10-20T03:05:34Z)
• 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)
• 3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection [35.5386998382886]
  3D visual grounding aims to locate the referred target object in 3D point cloud scenes according to a free-form language description. Previous methods mostly follow a two-stage paradigm, i.e., language-irrelevant detection and cross-modal matching. We propose a 3D Single-Stage Referred Point Progressive Selection method, which progressively selects keypoints with the guidance of language and directly locates the target.
  arXiv  Detail & Related papers  (2022-04-13T09:46:27Z)
• 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)
• Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds [29.762645632148097]
  We propose a highly-efficient single-stage point-based 3D detector called IA-SSD. We exploit two learnable, task-oriented, instance-aware downsampling strategies to hierarchically select the foreground points. Experiments conducted on several large-scale detection benchmarks demonstrate the competitive performance of our IA-SSD.
  arXiv  Detail & Related papers  (2022-03-21T17:14:02Z)
• 3D Object Detection Combining Semantic and Geometric Features from Point Clouds [19.127930862527666]
  We propose a novel end-to-end two-stage 3D object detector named SGNet for point clouds scenes. The VTPM is a Voxel-Point-Based Module that finally implements 3D object detection in point space. As of September 19, 2021, for KITTI dataset, SGNet ranked 1st in 3D and BEV detection on cyclists with easy difficulty level, and 2nd in the 3D detection of moderate cyclists.
  arXiv  Detail & Related papers  (2021-10-10T04:43:27Z)
• 3D Spatial Recognition without Spatially Labeled 3D [127.6254240158249]
  We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition. We show that WyPR can detect and segment objects in point cloud data without access to any spatial labels at training time.
  arXiv  Detail & Related papers  (2021-05-13T17:58:07Z)
• Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection [102.62963605429508]
  Point cloud semantic segmentation plays an essential role in autonomous driving. Current 3D semantic segmentation networks focus on convolutional architectures that perform great for well represented classes. We propose a novel Aware 3D Semantic Detection (DASS) framework that explicitly leverages localization features from an auxiliary 3D object detection task.
  arXiv  Detail & Related papers  (2020-09-22T14:17:40Z)
• InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling [65.47126868838836]
  We propose a novel 3D object detection framework with dynamic information modeling. Coarse predictions are generated in the first stage via a voxel-based region proposal network. Experiments are conducted on the large-scale nuScenes 3D detection benchmark.
  arXiv  Detail & Related papers  (2020-07-16T18:27:08Z)
• 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.