Hyperbolic Hierarchical Alignment Reasoning Network for Text-3D Retrieval

• URL: http://arxiv.org/abs/2511.11045v1
• Date: Fri, 14 Nov 2025 07:59:57 GMT
• Title: Hyperbolic Hierarchical Alignment Reasoning Network for Text-3D Retrieval
• Abstract summary: We introduce the Hyperbolic Hierarchical Alignment Reasoning Network (H$2$ARN) for text-3D retrieval.<n>H$2$ARN embeds both text and 3D data in a Lorentz-model hyperbolic space.<n>We also release the expanded T3DR-HIT v2 benchmark, which contains 8,935 text-to-3D pairs, 2.6 times the original size.
• Score: 38.14508593270809
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the daily influx of 3D data on the internet, text-3D retrieval has gained increasing attention. However, current methods face two major challenges: Hierarchy Representation Collapse (HRC) and Redundancy-Induced Saliency Dilution (RISD). HRC compresses abstract-to-specific and whole-to-part hierarchies in Euclidean embeddings, while RISD averages noisy fragments, obscuring critical semantic cues and diminishing the model's ability to distinguish hard negatives. To address these challenges, we introduce the Hyperbolic Hierarchical Alignment Reasoning Network (H$^{2}$ARN) for text-3D retrieval. H$^{2}$ARN embeds both text and 3D data in a Lorentz-model hyperbolic space, where exponential volume growth inherently preserves hierarchical distances. A hierarchical ordering loss constructs a shrinking entailment cone around each text vector, ensuring that the matched 3D instance falls within the cone, while an instance-level contrastive loss jointly enforces separation from non-matching samples. To tackle RISD, we propose a contribution-aware hyperbolic aggregation module that leverages Lorentzian distance to assess the relevance of each local feature and applies contribution-weighted aggregation guided by hyperbolic geometry, enhancing discriminative regions while suppressing redundancy without additional supervision. We also release the expanded T3DR-HIT v2 benchmark, which contains 8,935 text-to-3D pairs, 2.6 times the original size, covering both fine-grained cultural artefacts and complex indoor scenes. Our codes are available at https://github.com/liwrui/H2ARN.

Related papers

• USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes [57.4583999658488]
  Spatial representation learning for autonomous driving aims to map raw visual signals into structured 3D scene representations.<n>Existing methods treat dynamic reconstruction and instance-level perception as separate tasks.<n>We propose USR-Drive, a unified conditional generative framework that recovers dense dynamic geometry and instance-level object layouts within a shared scene representation.
  arXiv  Detail & Related papers  (2026-08-19T15:29:06Z)
• FDR-Occ: Factorized Dense Routing for Full-Spectrum 3D Occupancy Prediction [63.738631229597274]
  Vision-based 3D occupancy prediction fundamentally relies on the 2D-to-3D view transformation.<n>Current paradigms predominantly utilize explicit physical projection, which artificially restricts the routing matrix to strict, sparse camera rays.<n>We propose Factorized Dense Routing (FDR) to break this bottleneck.<n>Our framework achieves state-the-art performance on the Occ3D-nuScenes and Occ3D-Waymo benchmarks.
  arXiv  Detail & Related papers  (2026-07-04T11:12:42Z)
• PC-CrossDiff: Point-Cluster Dual-Level Cross-Modal Differential Attention for Unified 3D Referring and Segmentation [40.5034963034718]
  3D Visual Grounding aims to localize the referent of natural language referring expressions through two core tasks: Referring Expression (3DREC) and (3DRES)<n>Existing methods face two key challenges in complex, multi-object scenes: inadequate parsing of implicit localization cues and ineffective suppression of dynamic spatial interference from co-occurring objects.<n>We propose PC-CrossDiff, a unified dual-task framework with a dual-level cross-modal differential attention architecture for 3DREC and 3DRES.
  arXiv  Detail & Related papers  (2026-03-18T14:16:48Z)
• HeRO: Hierarchical 3D Semantic Representation for Pose-aware Object Manipulation [54.325346533275074]
  HeRO is a diffusion-based policy that couples geometry and semantics via hierarchical semantic fields.<n>In various tests, HeRO establishes a new state-of-the-art, improving success on Place Dual Shoes by 12.3% and averaging 6.5% gains across six challenging pose-aware tasks.
  arXiv  Detail & Related papers  (2026-02-21T12:29:10Z)
• Mono3DVG-EnSD: Enhanced Spatial-aware and Dimension-decoupled Text Encoding for Monocular 3D Visual Grounding [42.41930714202838]
  We propose Mono3DVG-EnSD, a novel framework that integrates two key components: the CLIP-Guided Lexical Certainty Adapter (CLIP-LCA) and the Dimension-Decoupled Module (D2M)<n>Our method achieves state-of-the-art (SOTA) performance across all metrics. Notably, it improves the challenging Far(Acc@0.5) scenario by a significant +13.54%.
  arXiv  Detail & Related papers  (2025-11-10T10:02:30Z)
• IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction [82.53307702809606]
  Humans naturally perceive the geometric structure and semantic content of a 3D world as intertwined dimensions.<n>We propose InstanceGrounded Geometry Transformer (IGGT) to unify the knowledge for both spatial reconstruction and instance-level contextual understanding.
  arXiv  Detail & Related papers  (2025-10-26T14:57:44Z)
• Where, Not What: Compelling Video LLMs to Learn Geometric Causality for 3D-Grounding [0.8883733362171032]
  We propose a novel training framework called What-Where Representation Re-Forming (W2R2) to tackle this issue.<n>Our approach fundamentally reshapes the model's internal space by designating 2D features as semantic beacons for "What" identification and 3D features as spatial anchors for "Where" localization.<n>Experiments conducted on ScanRefer and ScanQA demonstrate the effectiveness of W2R2, with significant gains in localization accuracy and robustness.
  arXiv  Detail & Related papers  (2025-10-19T22:40:18Z)
• Combined Hyperbolic and Euclidean Soft Triple Loss Beyond the Single Space Deep Metric Learning [0.0]
  We find that the combination of hyperbolic and Euclidean spaces improves DML accuracy and learning stability for both spaces.<n>We evaluate the CHEST loss on four benchmark datasets, achieving a new state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-10-07T07:42:49Z)
• NeuraLoc: Visual Localization in Neural Implicit Map with Dual Complementary Features [50.212836834889146]
  We propose an efficient and novel visual localization approach based on the neural implicit map with complementary features.<n>Specifically, to enforce geometric constraints and reduce storage requirements, we implicitly learn a 3D keypoint descriptor field.<n>To further address the semantic ambiguity of descriptors, we introduce additional semantic contextual feature fields.
  arXiv  Detail & Related papers  (2025-03-08T08:04:27Z)
• Escaping Plato's Cave: Towards the Alignment of 3D and Text Latent Spaces [52.237827968294766]
  We show that naive post-training feature alignment of uni-modal text and 3D encoders results in limited performance.<n>We then focus on extracting subspaces of the corresponding feature spaces and discover that by projecting learned representations onto well-chosen lower-dimensional subspaces the quality of alignment becomes significantly higher.<n>Ours is the first work that helps to establish a baseline for post-training alignment of 3D uni-modal and text feature spaces.
  arXiv  Detail & Related papers  (2025-03-07T09:51:56Z)
• Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding [59.51535163599723]
  FreeGS is an unsupervised semantic-embedded 3DGS framework that achieves view-consistent 3D scene understanding without the need for 2D labels.<n>FreeGS performs comparably to state-of-the-art methods while avoiding the complex data preprocessing workload.
  arXiv  Detail & Related papers  (2024-11-29T08:52:32Z)
• Riemann-based Multi-scale Attention Reasoning Network for Text-3D Retrieval [14.775984198185556]
  We propose a novel Multi-scale Attention Reasoning Network (RMARN) for text-3D retrieval.<n>RMARN learns the manifold parameters to better represent the distances between text-point cloud samples.<n>To address the challenges of lacking paired text-3D data, we have created the large-scale Text-3D Retrieval dataset T3DR-HIT.
  arXiv  Detail & Related papers  (2024-08-25T03:21:48Z)
• Learning Hyperbolic Representations for Unsupervised 3D Segmentation [3.516233423854171]
  We propose learning effective representations of 3D patches for unsupervised segmentation through a variational autoencoder (VAE) with a hyperbolic latent space and a proposed gyroplane convolutional layer. We demonstrate the effectiveness of our hyperbolic representations for unsupervised 3D segmentation on a hierarchical toy dataset, BraTS whole tumor dataset, and cryogenic electron microscopy data.
  arXiv  Detail & Related papers  (2020-12-03T02:15:31Z)

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.