# MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes

arXiv: https://arxiv.org/abs/2203.05203 · HTML: https://mlsanity.com/paper/2203.05203/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 53 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language](https://mlsanity.com/paper/1912.08830/) (2019)
- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [Auto-Encoding Scene Graphs for Image Captioning](https://mlsanity.com/paper/1812.02378/) (2018)
- [Scan2Cap: Context-aware Dense Captioning in RGB-D Scans](https://mlsanity.com/paper/2012.02206/) (2020)
- [Exploring Visual Relationship for Image Captioning](https://mlsanity.com/paper/1809.07041/) (2018)
- [Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions](https://mlsanity.com/paper/2004.03967/) (2020)
- [X-Linear Attention Networks for Image Captioning](https://mlsanity.com/paper/2003.14080/) (2020)
- [SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences](https://mlsanity.com/paper/2103.14898/) (2021)
- [InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring](https://mlsanity.com/paper/2103.01128/) (2021)
- [SAT: 2D Semantics Assisted Training for 3D Visual Grounding](https://mlsanity.com/paper/2105.11450/) (2021)
- [Towards Deeper Graph Neural Networks with Differentiable Group Normalization](https://mlsanity.com/paper/2006.06972/) (2020)
- [TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding](https://mlsanity.com/paper/2108.02388/) (2021)
- [Grounding Referring Expressions in Images by Variational Context](https://mlsanity.com/paper/1712.01892/) (2017)
- [Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud](https://mlsanity.com/paper/2103.16381/) (2021)
- [Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning](https://mlsanity.com/paper/1804.00100/) (2018)
- [Consensus-Aware Visual-Semantic Embedding for Image-Text Matching](https://mlsanity.com/paper/2007.08883/) (2020)
- [Learning Modality Interaction for Temporal Sentence Localization and Event Captioning in Videos](https://mlsanity.com/paper/2007.14164/) (2020)
- [Jointly Modeling Embedding and Translation to Bridge Video and Language](https://mlsanity.com/paper/1505.01861/) (2015)
- [Dense Captioning with Joint Inference and Visual Context](https://mlsanity.com/paper/1611.06949/) (2016)

## Built on by (most cited first, 30 of 46)

- [LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning](https://mlsanity.com/paper/2311.18651/) (2023)
- [Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers](https://mlsanity.com/paper/2312.08168/) (2023)
- [Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes](https://mlsanity.com/paper/2308.08769/) (2023)
- [Grounded 3D-LLM with Referent Tokens](https://mlsanity.com/paper/2405.10370/) (2024)
- [EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding](https://mlsanity.com/paper/2209.14941/) (2022)
- [LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding](https://mlsanity.com/paper/2312.14074/) (2023)
- [End-to-End 3D Dense Captioning with Vote2Cap-DETR](https://mlsanity.com/paper/2301.02508/) (2023)
- [UniT3D: A Unified Transformer for 3D Dense Captioning and Visual Grounding](https://mlsanity.com/paper/2212.00836/) (2022)
- [3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer](https://mlsanity.com/paper/2501.01163/) (2025)
- [EventHallusion: Diagnosing Event Hallucinations in Video LLMs](https://mlsanity.com/paper/2409.16597/) (2024)
- [Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction Tuning](https://mlsanity.com/paper/2503.00513/) (2025)
- [ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities](https://mlsanity.com/paper/2407.01525/) (2024)
- [TOD3Cap: Towards 3D Dense Captioning in Outdoor Scenes](https://mlsanity.com/paper/2403.19589/) (2024)
- [MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations](https://mlsanity.com/paper/2406.09401/) (2024)
- [Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning](https://mlsanity.com/paper/2309.02999/) (2023)
- [Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding](https://mlsanity.com/paper/2305.10714/) (2023)
- [Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models](https://mlsanity.com/paper/2403.07304/) (2024)
- [3D Vision and Language Pretraining with Large-Scale Synthetic Data](https://mlsanity.com/paper/2407.06084/) (2024)
- [Contextual Modeling for 3D Dense Captioning on Point Clouds](https://mlsanity.com/paper/2210.03925/) (2022)
- [Dense Object Grounding in 3D Scenes](https://mlsanity.com/paper/2309.02224/) (2023)
- [DC-Scene: Data-Centric Learning for 3D Scene Understanding](https://mlsanity.com/paper/2505.15232/) (2025)
- [3D CoCa: Contrastive Learners are 3D Captioners](https://mlsanity.com/paper/2504.09518/) (2025)
- [Recent Advances in Multi-modal 3D Intelligence: A Comprehensive Survey and Evaluation](https://mlsanity.com/paper/2310.15676/) (2023)
- [A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes](https://mlsanity.com/paper/2403.07469/) (2024)
- [PerLA: Perceptive 3D Language Assistant](https://mlsanity.com/paper/2411.19774/) (2024)
- [MoE3D: Mixture of Experts meets Multi-Modal 3D Understanding](https://mlsanity.com/paper/2511.22103/) (2025)
- [Bi-directional Contextual Attention for 3D Dense Captioning](https://mlsanity.com/paper/2408.06662/) (2024)
- [CVP: Central-Peripheral Vision-Inspired Multimodal Model for Spatial Reasoning](https://mlsanity.com/paper/2512.08135/) (2025)
- [See It All: Contextualized Late Aggregation for 3D Dense Captioning](https://mlsanity.com/paper/2408.07648/) (2024)
- [ExCap3D: Expressive 3D Scene Understanding via Object Captioning with Varying Detail](https://mlsanity.com/paper/2503.17044/) (2025)

## Abstract (arXiv)

3D dense captioning is a recently-proposed novel task, where point clouds contain more geometric information than the 2D counterpart. However, it is also more challenging due to the higher complexity and wider variety of inter-object relations contained in point clouds. Existing methods only treat such relations as by-products of object feature learning in graphs without specifically encoding them, which leads to sub-optimal results. In this paper, aiming at improving 3D dense captioning via capturing and utilizing the complex relations in the 3D scene, we propose MORE, a Multi-Order RElation mining model, to support generating more descriptive and comprehensive captions. Technically, our MORE encodes object relations in a progressive manner since complex relations can be deduced from a limited number of basic ones. We first devise a novel Spatial Layout Graph Convolution (SLGC), which semantically encodes several first-order relations as edges of a graph constructed over 3D object proposals. Next, from the resulting graph, we further extract multiple triplets which encapsulate basic first-order relations as the basic unit, and construct several Object-centric Triplet Attention Graphs (OTAG) to infer multi-order relations for every target object. The updated node features from OTAG are aggregated and fed into the caption decoder to provide abundant relational cues, so that captions including diverse relations with context objects can be generated. Extensive experiments on the Scan2Cap dataset prove the effectiveness of our proposed MORE and its components, and we also outperform the current state-of-the-art method. Our code is available at https://github.com/SxJyJay/MORE.