Mapping the Unseen: Unified Promptable Panoptic Mapping with Dynamic Labeling using Foundation Models

• URL: http://arxiv.org/abs/2405.02162v4
• Date: Mon, 18 Aug 2025 14:04:47 GMT
• Title: Mapping the Unseen: Unified Promptable Panoptic Mapping with Dynamic Labeling using Foundation Models
• Authors: Mohamad Al Mdfaa, Raghad Salameh, Geesara Kulathunga, Sergey Zagoruyko, Gonzalo Ferrer,
• Abstract summary: We present Unified Promptable Panoptic Mapping (UPPM), which leverages foundation models for dynamic labeling without additional training.<n>UPPM attains exceptional geometry reconstruction accuracy (0.61cm on the Flat dataset), the highest panoptic quality (0.414), and better performance compared to state-of-the-art segmentation methods.
• Score: 2.914924674957017
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In robotics and computer vision, semantic mapping remains a critical challenge for machines to comprehend complex environments. Traditional panoptic mapping approaches are constrained by fixed labels, limiting their ability to handle novel objects. We present Unified Promptable Panoptic Mapping (UPPM), which leverages foundation models for dynamic labeling without additional training. UPPM is evaluated across three comprehensive levels: Segmentation-to-Map, Map-to-Map, and Segmentation-to-Segmentation. Results demonstrate UPPM attains exceptional geometry reconstruction accuracy (0.61cm on the Flat dataset), the highest panoptic quality (0.414), and better performance compared to state-of-the-art segmentation methods. Furthermore, ablation studies validate the contributions of unified semantics, custom NMS, and blurry frame filtering, with the custom NMS improving the completion ratio by 8.27% on the Flat dataset. UPPM demonstrates effective scene reconstruction with rich semantic labeling across diverse datasets.

Related papers

• Chart Specification: Structural Representations for Incentivizing VLM Reasoning in Chart-to-Code Generation [11.18352269863283]
  Vision-Language Models (VLMs) have shown promise in generating plotting code from chart images.<n>Existing approaches largely rely on supervised fine-tuning, encouraging surface-level token imitation.<n>We propose Chart Specification, a structured intermediate representation that shifts training from text imitation to semantically grounded supervision.
  arXiv  Detail & Related papers  (2026-02-11T14:08:06Z)
• Vocabulary-free Fine-grained Visual Recognition via Enriched Contextually Grounded Vision-Language Model [52.01031460230826]
  Traditional approaches rely heavily on fixed vocabularies and closed-set classification paradigms.<n>Recent research has demonstrated that combining large language models with vision-language models (VLMs) makes open-set recognition possible.<n>We propose our training-free method, Enriched-FineR, which demonstrates state-of-the-art results in fine-grained visual recognition.
  arXiv  Detail & Related papers  (2025-07-30T20:06:01Z)
• MapBERT: Bitwise Masked Modeling for Real-Time Semantic Mapping Generation [15.116320098263149]
  MapBERT is a novel framework designed to model the distribution of unseen spaces.<n>We show that MapBERT achieves state-of-the-art semantic map generation.<n> Experiments on Gibson benchmarks show that MapBERT achieves state-of-the-art semantic map generation.
  arXiv  Detail & Related papers  (2025-06-09T01:55:55Z)
• Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors [22.957096921873678]
  We develop a supervised training pipeline based on pseudo labels.<n>The pseudo labels are generated from an off-the-shelf part segmentation model, guided by a mapping from affordance to part names.<n>These techniques harness the semantic knowledge of static objects embedded in off-the-shelf foundation models to improve affordance learning.
  arXiv  Detail & Related papers  (2025-05-30T01:12:39Z)
• Leveraging Foundation Models for Multimodal Graph-Based Action Recognition [1.533133219129073]
  We introduce a graph-based framework that integrates a vision-temporal foundation leveraging VideoMAE for dynamic visual encoding and BERT for contextual textual embedding.<n>We show that our method consistently outperforms state-of-the-art baselines on diverse benchmark datasets.
  arXiv  Detail & Related papers  (2025-05-21T07:15:14Z)
• Map Feature Perception Metric for Map Generation Quality Assessment and Loss Optimization [2.311323886036968]
  This study introduces a novel Map Feature Metric designed to evaluate global characteristics and spatial congruence between synthesized and target maps.<n>Our approach extracts elemental-level deep features that comprehensively encode cartographic structural integrity and topological relationships.
  arXiv  Detail & Related papers  (2025-03-30T09:07:09Z)
• Context-Aware Semantic Segmentation: Enhancing Pixel-Level Understanding with Large Language Models for Advanced Vision Applications [0.0]
  We propose a novel Context-Aware Semantic framework that integrates Large Language Models (LLMs) with state-of-the-art vision backbones. A Cross-Attention Mechanism is introduced to align vision and language features, enabling the model to reason about context more effectively. This work bridges the gap between vision and language, paving the path for more intelligent and context-aware vision systems in applications including autonomous driving, medical imaging, and robotics.
  arXiv  Detail & Related papers  (2025-03-25T02:12:35Z)
• Learning and Evaluating Hierarchical Feature Representations [3.770103075126785]
  We propose a novel framework, Hierarchical Composition of Orthogonal Subspaces (Hier-COS)<n>Hier-COS learns to map deep feature embeddings into a vector space that is, by design, consistent with the structure of a given taxonomy tree.<n>We demonstrate that Hier-COS achieves state-of-the-art hierarchical performance across all the datasets while simultaneously beating top-1 accuracy in all but one case.
  arXiv  Detail & Related papers  (2025-03-10T20:59:41Z)
• Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies. Our findings are synthesized in Flex (Fly-lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors. We demonstrate the effectiveness of this approach on quadrotor fly-to-target tasks, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection [68.18620488664187]
  We propose a simple yet effective Semi-supervised Oriented Object Detection method termed SOOD++.<n> Specifically, we observe that objects from aerial images usually have arbitrary orientations, small scales, and dense distribution.<n>Extensive experiments conducted on various oriented object under various labeled settings demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-07-01T07:03:51Z)
• DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model [15.803614800117781]
  We propose DiffMap, a novel approach specifically designed to model the structured priors of map segmentation masks. By incorporating this technique, the performance of existing semantic segmentation methods can be significantly enhanced. Our model demonstrates superior proficiency in generating results that more accurately reflect real-world map layouts.
  arXiv  Detail & Related papers  (2024-05-03T11:16:27Z)
• Mapping High-level Semantic Regions in Indoor Environments without Object Recognition [50.624970503498226]
  The present work proposes a method for semantic region mapping via embodied navigation in indoor environments. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location.
  arXiv  Detail & Related papers  (2024-03-11T18:09:50Z)
• Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain [18.165807360855435]
  Graph Neural Networks (GNNs) have shown promise in learning dynamic functional connectivity for distinguishing phenotypes from human brain networks. We introduce the Spatio-Temporal Joint Embedding Masked Autoencoder (ST-JEMA), drawing inspiration from the Joint Embedding Predictive Architecture (JEPA) in computer vision.
  arXiv  Detail & Related papers  (2024-03-11T04:49:41Z)
• LISNeRF Mapping: LiDAR-based Implicit Mapping via Semantic Neural Fields for Large-Scale 3D Scenes [2.822816116516042]
  Large-scale semantic mapping is crucial for outdoor autonomous agents to fulfill high-level tasks such as planning and navigation. This paper proposes a novel method for large-scale 3D semantic reconstruction through implicit representations from posed LiDAR measurements alone.
  arXiv  Detail & Related papers  (2023-11-04T03:55:38Z)
• Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation [84.62067728093358]
  Weakly supervised object localization and semantic segmentation aim to localize objects using only image-level labels. New paradigm has emerged by generating a foreground prediction map to achieve pixel-level localization. This paper presents two astonishing experimental observations on the object localization learning process.
  arXiv  Detail & Related papers  (2023-09-22T15:44:10Z)
• A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition [4.701158597171363]
  The aim of this study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. We use a problem transformation approach, in which actions were subset into two biomechanically independent components.
  arXiv  Detail & Related papers  (2023-09-13T20:21:41Z)
• Knowledge-augmented Frame Semantic Parsing with Hybrid Prompt-tuning [17.6573121083417]
  We propose a Knowledge-Augmented Frame Semantic Parsing Architecture (KAF-SPA) to enhance semantic representation. A Memory-based Knowledge Extraction Module (MKEM) is devised to select accurate frame knowledge and construct the continuous templates. We also design a Task-oriented Knowledge Probing Module (TKPM) using hybrid prompts to incorporate the selected knowledge into the PLMs and adapt PLMs to the tasks of frame and argument identification.
  arXiv  Detail & Related papers  (2023-03-25T06:41:19Z)
• Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels [60.675714333081466]
  Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, ie, CLIP, to compensate for insufficient annotations. We advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior.
  arXiv  Detail & Related papers  (2023-03-23T12:39:20Z)
• Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles [63.20765930558542]
  3D semantic data are useful for core perception tasks such as obstacle detection and ego-vehicle localization. We propose a new dataset, Navya 3D (Navya3DSeg), with a diverse label space corresponding to a large scale production grade operational domain. It contains 23 labeled sequences and 25 supplementary sequences without labels, designed to explore self-supervised and semi-supervised semantic segmentation benchmarks on point clouds.
  arXiv  Detail & Related papers  (2023-02-16T13:41:19Z)
• UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection [52.91782218300844]
  We propose a novel Unsupervised Inconsistency-Aware method based on Vision Transformer, called UIA-ViT. Due to the self-attention mechanism, the attention map among patch embeddings naturally represents the consistency relation, making the vision Transformer suitable for the consistency representation learning.
  arXiv  Detail & Related papers  (2022-10-23T15:24:47Z)
• Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification [68.06496970320595]
  Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. We present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs.
  arXiv  Detail & Related papers  (2022-05-18T07:47:01Z)
• Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings [81.09026586111811]
  We propose an approach to semantic segmentation that achieves state-of-the-art supervised performance when applied in a zero-shot setting. This is achieved by replacing each class label with a vector-valued embedding of a short paragraph that describes the class. The resulting merged semantic segmentation dataset of over 2 Million images enables training a model that achieves performance equal to that of state-of-the-art supervised methods on 7 benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-04T07:19:09Z)
• Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching [19.706907816202946]
  We present a novel lightweight object-level mapping and localization method with high accuracy and robustness. We use object-level features with both semantic and geometric information to model landmarks in the environment. Based on the proposed map, the robust localization is achieved by constructing a novel local semantic scene graph descriptor.
  arXiv  Detail & Related papers  (2022-01-16T05:47:07Z)
• MSeg: A Composite Dataset for Multi-domain Semantic Segmentation [100.17755160696939]
  We present MSeg, a composite dataset that unifies semantic segmentation datasets from different domains. We reconcile the generalization and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images. A model trained on MSeg ranks first on the WildDash-v1 leaderboard for robust semantic segmentation, with no exposure to WildDash data during training.
  arXiv  Detail & Related papers  (2021-12-27T16:16:35Z)
• Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation [94.16816278191477]
  We present a framework for semi-adaptive and domain-supervised semantic segmentation. It is enhanced by self-supervised monocular depth estimation trained only on unlabeled image sequences. We validate the proposed model on the Cityscapes dataset.
  arXiv  Detail & Related papers  (2021-08-28T01:33:38Z)
• Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations [0.8203855808943658]
  In this work, we explore the possibility of generating synthetic data for neural semantic parsing. Specifically, we first extract masked templates from the existing labeled utterances, and then fine-tune BART to generate synthetic utterances conditioning. We show the potential of our approach when evaluating on the Facebook TOP dataset for navigation domain.
  arXiv  Detail & Related papers  (2020-11-03T22:55:40Z)

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.