Persistent Homology Meets Object Unity: Object Recognition in Clutter

• URL: http://arxiv.org/abs/2305.03815v3
• Date: Thu, 21 Dec 2023 07:29:43 GMT
• Title: Persistent Homology Meets Object Unity: Object Recognition in Clutter
• Authors: Ekta U. Samani, Ashis G. Banerjee
• Abstract summary: Recognition of occluded objects in unseen and unstructured indoor environments is a challenging problem for mobile robots. We propose a new descriptor, TOPS, for point clouds generated from depth images and an accompanying recognition framework, THOR, inspired by human reasoning. THOR outperforms state-of-the-art methods on both the datasets and achieves substantially higher recognition accuracy for all the scenarios of the UW-IS Occluded dataset.
• Score: 2.356908851188234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recognition of occluded objects in unseen and unstructured indoor environments is a challenging problem for mobile robots. To address this challenge, we propose a new descriptor, TOPS, for point clouds generated from depth images and an accompanying recognition framework, THOR, inspired by human reasoning. The descriptor employs a novel slicing-based approach to compute topological features from filtrations of simplicial complexes using persistent homology, and facilitates reasoning-based recognition using object unity. Apart from a benchmark dataset, we report performance on a new dataset, the UW Indoor Scenes (UW-IS) Occluded dataset, curated using commodity hardware to reflect real-world scenarios with different environmental conditions and degrees of object occlusion. THOR outperforms state-of-the-art methods on both the datasets and achieves substantially higher recognition accuracy for all the scenarios of the UW-IS Occluded dataset. Therefore, THOR, is a promising step toward robust recognition in low-cost robots, meant for everyday use in indoor settings.

Related papers

• DistFormer: Enhancing Local and Global Features for Monocular Per-Object Distance Estimation [35.6022448037063]
  Per-object distance estimation is crucial in safety-critical applications such as autonomous driving, surveillance, and robotics. Existing approaches rely on two scales: local information (i.e., the bounding box proportions) or global information. Our work aims to strengthen both local and global cues.
  arXiv  Detail & Related papers  (2024-01-06T10:56:36Z)
• Open World Object Detection in the Era of Foundation Models [53.683963161370585]
  We introduce a new benchmark that includes five real-world application-driven datasets. We introduce a novel method, Foundation Object detection Model for the Open world, or FOMO, which identifies unknown objects based on their shared attributes with the base known objects.
  arXiv  Detail & Related papers  (2023-12-10T03:56:06Z)
• ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection [70.11264880907652]
  Recent object (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. We propose an effective unified collaborative pyramid network that mimics human behavior when observing vague images and camouflaged zooming in and out. Our framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks.
  arXiv  Detail & Related papers  (2023-10-31T06:11:23Z)
• Human-Inspired Topological Representations for Visual Object Recognition in Unseen Environments [2.356908851188234]
  We propose a shape-based TOPS2 descriptor and a THOR2 framework for visual object recognition. THOR2, trained using synthetic data, achieves substantially higher recognition accuracy than the shape-based THOR framework. THOR2 is a promising step toward achieving robust recognition in low-cost robots.
  arXiv  Detail & Related papers  (2023-09-15T08:24:07Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Evaluation of Environmental Conditions on Object Detection using Oriented Bounding Boxes for AR Applications [7.274773183842099]
  Scene analysis and object recognition play a crucial role in augmented reality (AR) New approach is proposed that involves using oriented bounding boxes with a detection and recognition deep network to improve performance and processing time. Results indicate that the proposed approach tends to produce better Average Precision and greater accuracy for small objects in most of the tested conditions.
  arXiv  Detail & Related papers  (2023-06-29T09:17:58Z)
• Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments [1.2691047660244335]
  Recognition of occluded objects in unseen indoor environments is a challenging problem for mobile robots. This work proposes a new slicing-based topological descriptor that captures the 3D shape of object point clouds. It yields similarities between the descriptors of the occluded and the corresponding unoccluded objects, enabling object unity-based recognition.
  arXiv  Detail & Related papers  (2022-05-16T07:01:16Z)
• Complex-Valued Autoencoders for Object Discovery [62.26260974933819]
  We propose a distributed approach to object-centric representations: the Complex AutoEncoder. We show that this simple and efficient approach achieves better reconstruction performance than an equivalent real-valued autoencoder on simple multi-object datasets. We also show that it achieves competitive unsupervised object discovery performance to a SlotAttention model on two datasets, and manages to disentangle objects in a third dataset where SlotAttention fails - all while being 7-70 times faster to train.
  arXiv  Detail & Related papers  (2022-04-05T09:25:28Z)
• Discovering Objects that Can Move [55.743225595012966]
  We study the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. We choose to focus on dynamic objects -- entities that can move independently in the world.
  arXiv  Detail & Related papers  (2022-03-18T21:13:56Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• Visual Object Recognition in Indoor Environments Using Topologically Persistent Features [2.2344764434954256]
  Object recognition in unseen indoor environments remains a challenging problem for visual perception of mobile robots. We propose the use of topologically persistent features, which rely on the objects' shape information, to address this challenge. We implement the proposed method on a real-world robot to demonstrate its usefulness.
  arXiv  Detail & Related papers  (2020-10-07T06:04:17Z)

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.