Group-Free 3D Object Detection via Transformers

• URL: http://arxiv.org/abs/2104.00678v1
• Date: Thu, 1 Apr 2021 17:59:36 GMT
• Title: Group-Free 3D Object Detection via Transformers
• Authors: Ze Liu, Zheng Zhang, Yue Cao, Han Hu, Xin Tong
• Abstract summary: 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.
• Score: 26.040378025818416
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, directly detecting 3D objects from 3D point clouds has received increasing attention. To extract object representation from an irregular point cloud, existing methods usually take a point grouping step to assign the points to an object candidate so that a PointNet-like network could be used to derive object features from the grouped points. However, the inaccurate point assignments caused by the hand-crafted grouping scheme decrease the performance of 3D object detection. In this paper, we present a simple yet effective method for directly detecting 3D objects from the 3D point cloud. Instead of grouping local points to each object candidate, 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 \cite{vaswani2017attention}, where the contribution of each point is automatically learned in the network training. With an improved attention stacking scheme, our method fuses object features in different stages and generates more accurate object detection results. 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. The code and models are publicly available at \url{https://github.com/zeliu98/Group-Free-3D}

Related papers

• Surface-biased Multi-Level Context 3D Object Detection [1.9723551683930771]
  This work addresses the object detection task in 3D point clouds using a highly efficient, surface-biased, feature extraction method (wang2022rbgnet) We propose a 3D object detector that extracts accurate feature representations of object candidates and leverages self-attention on point patches, object candidates, and on the global scene in 3D scene.
  arXiv  Detail & Related papers  (2023-02-13T11:50:04Z)
• CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds [55.44204039410225]
  We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels. To recover the features of missed voxels due to incorrect voxel-wise segmentation, we build a fully sparse convolutional RoI pooling module.
  arXiv  Detail & Related papers  (2022-10-09T13:38:48Z)
• Neural Correspondence Field for Object Pose Estimation [67.96767010122633]
  We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict 3D object coordinates at pixels of the input image, the proposed method predicts 3D object coordinates at 3D query points sampled in the camera frustum.
  arXiv  Detail & Related papers  (2022-07-30T01:48:23Z)
• SparseDet: Towards End-to-End 3D Object Detection [12.3069609175534]
  We propose SparseDet for end-to-end 3D object detection from point cloud. As a new detection paradigm, SparseDet maintains a fixed set of learnable proposals to represent latent candidates. SparseDet achieves highly competitive detection accuracy while running with a more efficient speed of 34.5 FPS.
  arXiv  Detail & Related papers  (2022-06-02T09:49:53Z)
• 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)
• Point2Seq: Detecting 3D Objects as Sequences [58.63662049729309]
  We present a simple and effective framework, named Point2Seq, for 3D object detection from point clouds. We view each 3D object as a sequence of words and reformulate the 3D object detection task as decoding words from 3D scenes in an auto-regressive manner.
  arXiv  Detail & Related papers  (2022-03-25T00:20:31Z)
• 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)
• 3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds [1.9458156037869139]
  Lidar based 3D object detection and classification tasks are essential for autonomous driving. This paper proposes a 3D object detection method based on point cloud and image.
  arXiv  Detail & Related papers  (2020-04-21T04:32:36Z)
• DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes [54.239416488865565]
  We propose a fast single-stage 3D object detection method for LIDAR data. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. We find that our proposed method achieves state-of-the-art results by 5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Open dataset.
  arXiv  Detail & Related papers  (2020-04-02T17:48:50Z)
• 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.