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Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers
Haifeng Huang, Yilun Chen, Zehan Wang, Rongjie Huang, Runsen Xu, Tai Wang, Luping Liu, Xize Cheng, Yang Zhao, Jiangmiao Pang, Zhou Zhao
TL;DR
3D MLLMs lack general object referencing and grounding, while scarce scene-language data limits learning complex 3D relationships. Chat-Scene addresses this gap with object identifiers and object-centric 2D/3D embeddings in a unified question-answering framework. It outperforms previous methods across five benchmarks with minimal downstream fine-tuning.
Problem
3D MLLMs struggle with general object referencing and grounding, while scarce scene-language data limits alignment between 3D scenes and language.
Method
Chat-Scene decomposes scenes into object proposals with identifier tokens and represents them using explicit object-centric embeddings from well-trained 2D and 3D models.
Results
Chat-Scene significantly outperforms existing methods across ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D, including 3.7% higher Acc@0.5 on ScanRefer.
Takeaways & Limitations
Object identifiers unify diverse 3D scene-language tasks as question-answering interactions, enabling joint training without additional task-specific heads.
Takeaways & Limitations
The method relies on frozen pre-trained 2D/3D detectors and encoders that occasionally produce incorrect results, including object misclassification.
Abstract
from arXiv · showhide
Recent advancements in 3D Large Language Models (LLMs) have demonstrated promising capabilities for 3D scene understanding. However, previous methods exhibit deficiencies in general referencing and grounding capabilities for intricate scene comprehension. In this paper, we introduce the use of object identifiers and object-centric representations to interact with scenes at the object level. Specifically, we decompose the input 3D scene into a set of object proposals, each assigned a unique identifier token, which enables efficient object referencing and grounding during user-assistant interactions. Given the scarcity of scene-language data, we model the scene embeddings as a sequence of explicit object-level embeddings, derived from semantic-rich 2D or 3D representations. By employing object identifiers, we transform diverse 3D scene-language tasks into a unified question-answering format, facilitating joint training without the need for additional task-specific heads. With minimal fine-tuning on all downstream tasks, our model significantly outperforms existing methods on benchmarks including ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D.
1 Introduction
3D MLLMs struggle with general object referencing and grounding, partly because scene-language data is scarce and scene representations obscure individual instances. Chat-Scene addresses these challenges with object identifiers and object-centric embeddings, achieving stronger performance across five benchmarks.
- Motivation: 3D MLLMs often fail to precisely reference or localize objects, limiting performance on grounding and dense-captioning tasks.Object referencing concerns understanding a specified object's semantics, while grounding concerns localizing it in the scene.
- Motivation: 3D grounding datasets contain only tens of thousands of instances, far fewer than the million-level datasets used for 2D MLLMs.The greater complexity of 3D spaces may require substantially more data to train location tokens robustly.
- Approach: Chat-Scene represents scenes at the object level by decomposing them into detector-generated proposals and assigning each proposal a unique object identifier.The design targets efficient object referencing and grounding during user-assistant interaction.
- Approach: Object identifiers convert diverse 3D scene-language tasks into unified question-answering pairs, enabling joint training without task-specific heads.This unification supports referencing and grounding across different task formulations.
- Approach: The model uses explicit object-level embeddings derived from well-trained 2D and 3D representations to reduce dependence on scarce scene-language data.This replaces hidden scene-level embeddings that do not efficiently interpret individual object instances.
- Results: 3.7% (Acc@0.5) on ScanRefer, 14.0% (F1@0.5) on Multi3DRefer, 8.7% (CIDEr@0.5) on Scan2Cap, and 8.4% (CIDEr) on ScanQA are the reported improvements over previous methods.Experiments cover five representative 3D scene-language datasets without task-specific fine-tuning.
2 Related Work
3D scene-language understanding uses language to interpret user intentions in 3D environments, spanning grounding, dense captioning, and visual question answering. Existing multimodal models handle objects or multimodal alignment well but struggle with complex spatial relationships and 3D structure.
- 3D Scene-language Understanding: 3D scene-language understanding uses language as contextual knowledge and query conditions for interpreting 3D environments.Its main tasks include visual grounding, dense captioning, and visual question answering.
- 3D Scene-language Understanding: 3D visual grounding locates queried objects, dense captioning combines localization with object descriptions, and visual question answering answers general scene questions.These tasks provide distinct forms of object- and scene-level understanding.
- Prior Work: Earlier task-specific methods had limited generalizability, while 3DJCG and D3Net unified grounding and dense captioning to exploit their complementary benefits.The passage presents these methods as efforts toward broader task integration.
- Prior Work: Existing multimodal models perform well at the object level but struggle with complex 3D spatial relationships and interactions among objects.Using 2D encoders can also make it difficult to capture 3D spatial structure.
3 Method
The method represents 3D scenes at the object level, combining proposal-specific 2D and 3D features with learnable object identifiers before feeding them to an LLM. It unifies diverse scene-language tasks as single-turn interactions, using shared language-model training without extra task-specific heads.
- Object Proposals: 3D scenes are decomposed into object proposals with a pre-trained Mask3D detector, whose instance masks support projection onto multi-view images.The i-th object is represented by its point cloud, and accurate masks are used for subsequent 2D projection.
- Object Identifiers: Learnable <OBJi> tokens are paired one-to-one with object features, forming object-level scene embeddings that preserve object identity for language-model interaction.The paired embeddings are fed into the LLM as a sequence representing the whole scene.
- Object-Centric Representations: Pre-trained Uni3D and DINOv2 encoders extract spatial, shape, semantic, and visual features for each object from point clouds and masked multi-view images.The resulting object-centric representations are projected into the language model’s token embedding space through 3D- and 2D-language projectors.
- Object Identifiers: Object identifiers let users reference specific objects and assistants ground target objects directly during interactions.The prompt encodes each proposal as “<OBJi> <object>”, with the identifier token linked to the corresponding object representation.
- Unified Tasks: Scene-language tasks are standardized as single-turn user-assistant question-answering interactions, enabling joint training without additional task-specific heads.The training corpus includes visual grounding, dense captioning, and visual question answering datasets.
- Training Strategy: The joint-training phase uses only the language model’s cross-entropy loss while concurrently training the projectors, identifier embeddings, and language model.This single-stage strategy avoids a separate projector-only alignment phase.
4 Experiments
Experiments evaluate Chat-Scene across five 3D scene-language benchmarks and ablations of object identifiers, multimodal object features, and video-input processing. The model improves state-of-the-art performance across tasks without task-specific fine-tuning, while video results expose tracking-related mask limitations.
- Datasets and Metrics: Five benchmarks cover single- and multi-object grounding, dense captioning, and visual question answering on ScanNet-based indoor scenes.The datasets are ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D.
- Datasets and Metrics: The evaluation uses thresholded accuracy for ScanRefer, F1 at IoU thresholds for Multi3DRefer, and CIDEr-based metrics for Scan2Cap, with benchmark metrics for VQA.ScanRefer uses Acc@0.25 and Acc@0.5; Multi3DRefer uses F1@0.25 and F1@0.5.
- Comparison with State-of-the-art Methods: The model improves state-of-the-art results by 3.7% Acc@0.5 on ScanRefer, 14.0% F1@0.5 on Multi3DRefer, and 8.7% CIDEr@0.5 on Scan2Cap without task-specific fine-tuning.It also achieves an 8.4% CIDEr improvement on ScanQA.
- Ablation Study: Learnable object-identifier tokens improve performance while reducing identifier cost from 6N to 3N for scenes with numerous objects.Plain-text identifiers such as “Obj001” require four tokens, and two object tokens per object require six tokens total.
- Ablation Study: Multi-view 2D features outperform single 3D features, while combining 3D and 2D features improves performance over either modality alone.Early Fusion reduces token cost but causes a slight performance drop relative to keeping modality-specific tokens separate.
- Experiments with 2D Video Input: Video experiments use tracked object proposals and evaluate grounding with spatial-temporal IoU; tracking failures can omit target-object frames and lower Acc@0.5.Despite lower-quality video objects, VQA results remain comparable to those using objects extracted from 3D inputs.
5 Conclusion
The paper models and interacts with 3D scenes at the object level to improve object referencing and grounding. Object identifiers unify diverse scene-language tasks as question-answering problems, while object-centric embeddings address scene-language data scarcity.
- The model decomposes input 3D scenes into object proposals assigned unique object identifiers for object-level interaction.
- Semantic-rich 2D and 3D representations provide explicit object-level embeddings for scene modeling under scarce scene-language data.
- Object identifiers convert diverse 3D scene-language tasks into unified question-answering pairs without additional task-specific heads.
- With minimal fine-tuning, the model significantly outperforms existing methods across ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D.
A Implementation Details
The implementation extracts object proposals and combines pretrained 3D and 2D representations. Object-specific 2D features are obtained by aggregating image patch features intersecting each object mask.
- Mask3D extracts 100 object proposals from 3D point clouds, while DEVA extracts video proposals averaging 48 objects.
- Uni3D supplies 3D features and DINOv2 supplies 2D features for the extracted object proposals.
- DINOv2 produces one CLS feature and 256 patch features per image, with each patch representing a 16×16 local image area.
- For each object mask, the method extracts and averages only patch features from image patches intersecting that mask.
B Quantitative Results
The evaluation covers five 3D scene-language benchmarks using their respective original metrics and compares the model with comprehensive state-of-the-art baselines.
- Evaluation spans ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D.
- Each benchmark uses the evaluation metrics proposed in its original publication.
- Performance comparisons against state-of-the-art methods appear in Tables 6–10, respectively covering the five benchmarks.
C Qualitative Analysis
The qualitative analysis focuses on ScanQA and ScanRefer, while the supplied table captions identify validation-set comparisons for Multi3DRefer and Scan2Cap.
- The qualitative analysis examines ScanQA and ScanRefer.
- Additional qualitative analysis of other datasets is planned for the final version.
- The Multi3DRefer results are presented as a validation-set performance comparison.
- The Scan2Cap results are presented as a validation-set performance comparison.
C.1 3D Question Answering
The ScanQA evaluations test appearance perception, descriptive identification, spatial description, and object counting. Chat-Scene performs relatively well on the first three but struggles with counting many target objects, compounded by ambiguous annotations.
- ScanQA evaluates color perception, descriptive object identification, spatial description, and object counting.The four examples cover distinct question-answering capabilities, including identifying targets from descriptions and counting objects.
- Chat-Scene performs relatively well on appearance, identification, and positional questions but struggles when many target objects must be counted.Counting requires accurate perception and localization of each object.
- Ambiguous ScanQA annotations, such as the undefined meaning of “right,” can lead to model confusion.The paper identifies annotation quality as an additional challenge for counting-related questions.
C.2 3D Visual Grounding
The visual-grounding section examines localizing objects from descriptions and compares interaction formats enabled by object identifiers. The examples expose failures involving scene structure, object attributes, incomplete point clouds, and ambiguous annotations.
- C.2 3D Visual Grounding: ScanRefer asks the model to localize a target object from a descriptive prompt; simpler examples are handled adequately, while harder cases remain challenging.The section presents six qualitative grounding examples.
- C.2 3D Visual Grounding: The model may miss relational descriptions such as “against the wall” because structural surfaces are not typically treated as training objects.The paper argues that walls, ceilings, and floors matter for comprehensive 3D scene understanding.
- C.2 3D Visual Grounding: Grounding can fail on object attributes, similar appearances, and incomplete point clouds, including confusing pillow colors or windows with doors.These cases illustrate errors beyond basic object localization.
- C.2 3D Visual Grounding: Some ScanRefer descriptions may match multiple objects, revealing ambiguity in the dataset annotations.The paper notes that a selection can satisfy the description even when it differs from the annotated target.
- D Input/Output Format Comparison: Object identifiers support input/output formats involving single or multiple object references across varied 3D scene-language tasks.Figure 11 compares the proposed format with expert models and 3D MLLMs, while acknowledging that some complex examples were not directly produced by the model.
E Limitation and Societal Impact
The paper identifies frozen foundation models and scarce, incomplete training coverage as limitations. It also notes potential hallucination risks when deployment scenarios exceed the training data.
- Limitations: The method relies on frozen pretrained 2D/3D detectors and encoders that can occasionally produce incorrect results.Future work proposes integrating these foundation models into end-to-end training.
- Limitations: Scarce 3D-language data can cause misclassification of underrepresented object classes such as “hair dryer” and “soap dish.”The paper suggests increasing data volume to improve scene understanding.
- Societal Impact: The paper reports consistent improvements across multiple 3D indoor scene-understanding tasks with potential downstream applications.The stated societal-impact discussion limits this claim to potential applicability.
- Societal Impact: Because training data does not cover every scenario, predictions may hallucinate and pose risks in system applications.The paper frames this as a potential societal-impact concern rather than a guaranteed failure.