Beyond Viewpoint: Robust 3D Object Recognition under Arbitrary Views through Joint Multi-Part Representation

• URL: http://arxiv.org/abs/2407.03842v2
• Date: Wed, 17 Jul 2024 17:52:05 GMT
• Title: Beyond Viewpoint: Robust 3D Object Recognition under Arbitrary Views through Joint Multi-Part Representation
• Authors: Linlong Fan, Ye Huang, Yanqi Ge, Wen Li, Lixin Duan,
• Abstract summary: Part-aware Network (PANet) aims to localize and understand different parts of 3D objects, such as airplane wings and tails. Our results on benchmark datasets clearly demonstrate that our proposed method outperforms existing view-based aggregation baselines for the task of 3D object recognition under arbitrary views.
• Score: 22.8031613567025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing view-based methods excel at recognizing 3D objects from predefined viewpoints, but their exploration of recognition under arbitrary views is limited. This is a challenging and realistic setting because each object has different viewpoint positions and quantities, and their poses are not aligned. However, most view-based methods, which aggregate multiple view features to obtain a global feature representation, hard to address 3D object recognition under arbitrary views. Due to the unaligned inputs from arbitrary views, it is challenging to robustly aggregate features, leading to performance degradation. In this paper, we introduce a novel Part-aware Network (PANet), which is a part-based representation, to address these issues. This part-based representation aims to localize and understand different parts of 3D objects, such as airplane wings and tails. It has properties such as viewpoint invariance and rotation robustness, which give it an advantage in addressing the 3D object recognition problem under arbitrary views. Our results on benchmark datasets clearly demonstrate that our proposed method outperforms existing view-based aggregation baselines for the task of 3D object recognition under arbitrary views, even surpassing most fixed viewpoint methods.

Related papers

• Variational Inference for Scalable 3D Object-centric Learning [19.445804699433353]
  We tackle the task of scalable unsupervised object-centric representation learning on 3D scenes. Existing approaches to object-centric representation learning show limitations in generalizing to larger scenes. We propose to learn view-invariant 3D object representations in localized object coordinate systems.
  arXiv  Detail & Related papers  (2023-09-25T10:23:40Z)
• CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection [57.44434974289945]
  We propose Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D) framework. Our framework takes a 3D scene as input and strives to explicitly integrate useful contextual information of the scene. In addition to 3D object detection, we investigate the effectiveness of our framework for the problem of 3D object counting.
  arXiv  Detail & Related papers  (2022-09-13T05:26:09Z)
• Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views [20.42021463570052]
  We focus on recognizing 3D shapes from arbitrary views, i.e., arbitrary numbers and positions of viewpoints. It is a challenging and realistic setting for view-based 3D shape recognition. We propose a canonical view representation to tackle this challenge.
  arXiv  Detail & Related papers  (2021-08-16T13:40:18Z)
• CoCoNets: Continuous Contrastive 3D Scene Representations [21.906643302668716]
  This paper explores self-supervised learning of amodal 3D feature representations from RGB and RGB-D posed images and videos. We show the resulting 3D visual feature representations effectively scale across objects and scenes, imagine information occluded or missing from the input viewpoints, track objects over time, align semantically related objects in 3D, and improve 3D object detection.
  arXiv  Detail & Related papers  (2021-04-08T15:50:47Z)
• Objects are Different: Flexible Monocular 3D Object Detection [87.82253067302561]
  We propose a flexible framework for monocular 3D object detection which explicitly decouples the truncated objects and adaptively combines multiple approaches for object depth estimation. Experiments demonstrate that our method outperforms the state-of-the-art method by relatively 27% for the moderate level and 30% for the hard level in the test set of KITTI benchmark.
  arXiv  Detail & Related papers  (2021-04-06T07:01:28Z)
• Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations [61.870882736758624]
  We propose a novel self-supervised paradigm to learn Multi-View Transformation Equivariant Representations (MV-TER) Specifically, we perform a 3D transformation on a 3D object, and obtain multiple views before and after the transformation via projection. Then, we self-train a representation to capture the intrinsic 3D object representation by decoding 3D transformation parameters from the fused feature representations of multiple views before and after the transformation.
  arXiv  Detail & Related papers  (2021-03-01T06:24:17Z)
• 3D Object Classification on Partial Point Clouds: A Practical Perspective [91.81377258830703]
  A point cloud is a popular shape representation adopted in 3D object classification. This paper introduces a practical setting to classify partial point clouds of object instances under any poses. A novel algorithm in an alignment-classification manner is proposed in this paper.
  arXiv  Detail & Related papers  (2020-12-18T04:00:56Z)
• 3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations [29.61554189447989]
  We propose a system that learns to detect objects and infer their 3D poses in RGB-D images. Many existing systems can identify objects and infer 3D poses, but they heavily rely on human labels and 3D annotations.
  arXiv  Detail & Related papers  (2020-10-30T13:56:09Z)
• Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild [40.132988301147776]
  We tackle the problems of few-shot object detection and few-shot viewpoint estimation. We demonstrate on both tasks the benefits of guiding the network prediction with class-representative features extracted from data. Our method outperforms state-of-the-art methods by a large margin on a range of datasets.
  arXiv  Detail & Related papers  (2020-07-23T16:17:25Z)
• Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions [70.0171362989609]
  We propose a novel fine-grained 3D shape classification method named FG3D-Net to capture the fine-grained local details of 3D shapes from multiple rendered views. Our results under the fine-grained 3D shape dataset show that our method outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-05-26T06:53:19Z)
• Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds [109.0016923028653]
  We learn point cloud representation by bidirectional reasoning between the local structures and the global shape without human supervision. We show that our unsupervised model surpasses the state-of-the-art supervised methods on both synthetic and real-world 3D object classification datasets.
  arXiv  Detail & Related papers  (2020-03-29T08:26:08Z)

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.