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LanguageRefer: Spatial-Language Model for 3D Visual Grounding
Junha Roh, Karthik Desingh, Ali Farhadi, Dieter Fox
TL;DR
The paper addresses how to ground referential language in noisy real-world 3D scenes, a challenge for robots interpreting instructions. LanguageRefer combines language and bounding-box spatial embeddings to select a target object. The authors report that it outperformed state-of-the-art models on ReferIt3D without additional training data, and analyze class-label noise and viewpoint effects.
Problem
Grounding varied referential language in noisy reconstructed 3D scenes is challenging, including when view-dependent expressions leave speaker orientation ambiguous.
Source: §1 · p. 2Method
Results
The authors report that LanguageRefer outperformed state-of-the-art models on ReferIt3D with no additional training data.
Source: §5 · p. 8Takeaways & Limitations
The authors analyze how noisy class labels and arbitrary viewpoints affect performance and examine transfer to different datasets in the context of future robotics applications.
Source: §1 · p. 2, §5 · p. 8Some view-dependent utterances lack enough annotation information to infer a valid speaker orientation, limiting the reliability of spatial relations such as “left of.”
Source: §1 · p. 2
Abstract
from arXiv · showhide
For robots to understand human instructions and perform meaningful tasks in the near future, it is important to develop learned models that comprehend referential language to identify common objects in real-world 3D scenes. In this paper, we introduce a spatial-language model for a 3D visual grounding problem. Specifically, given a reconstructed 3D scene in the form of point clouds with 3D bounding boxes of potential object candidates, and a language utterance referring to a target object in the scene, our model successfully identifies the target object from a set of potential candidates. Specifically, LanguageRefer uses a transformer-based architecture that combines spatial embedding from bounding boxes with fine-tuned language embeddings from DistilBert to predict the target object. We show that it performs competitively on visio-linguistic datasets proposed by ReferIt3D. Further, we analyze its spatial reasoning task performance decoupled from perception noise, the accuracy of view-dependent utterances, and viewpoint annotations for potential robotics applications.
1 Introduction
The paper frames 3D visual grounding as a step toward robot navigation from referential instructions, while highlighting challenges from noisy scenes, varied language, and ambiguous viewpoints. It introduces a modular spatial-language model and analyzes performance with class-label and viewpoint considerations.
1 Introduction
- The paper identifies noisy reconstructed scenes, diverse language, viewpoint inference, and their combined complexity as challenges for 3D reference.These challenges complicate analysis of what a model learns or understands.Source: §1 · p. 2
- The authors assume pre-trained instance classification or ground-truth object classes to separate spatial reasoning from object perception and examine perception noise’s role.Their focus is how language with spatial information handles reference, rather than an integrated multimodal perception system.Source: §1 · p. 2
- LanguageRefer achieved comparable scores on Nr3D and Sr3D with predicted class labels, and high accuracy on Sr3D with ground-truth labels.The authors interpret the latter as better understanding of template-based language data.Source: §1 · p. 2
- View-dependent utterances can lack enough information to infer a valid speaker orientation because speakers and listeners freely rotate scenes during annotation.The paper adds orientation annotations for view-dependent utterances and compares models with and without viewpoint correction.Source: §1 · p. 2
- The paper contributes a modular transformer-based model, viewpoint-orientation analysis, and ground-truth-class experiments that decouple perception noise from spatial reasoning.Its long-term goal is robot navigation in indoor environments based on referential instructions.Source: §1 · p. 2
2 Related Work
The related work connects object-centric spatial understanding to navigation and positions 3D grounding alongside 2D grounding approaches. LanguageRefer uses a pretrained language-model embedding space and semantic class labels in a modular alternative to more manually designed or multi-BERT approaches.
2.1 Vision-and-Language Navigation and Robot Navigation
- The paper treats 3D visual grounding as a proxy for continuous-space indoor navigation, focusing on spatial relationships among objects.Object-centric spatial understanding is described as important for navigation involving high-level semantic tasks.Source: §2.1 · p. 3
2.2 2D and 3D Visual Grounding
- LanguageRefer uses pretrained language embeddings and predicted semantic labels, reporting comparable Nr3D and Sr3D performance while retaining a modular perception component.The authors contrast its limited manual design with approaches using bird’s-eye-view operations, scene graphs, or auxiliary 2D images.Source: §2.2 · p. 3
3 Problem Statement and Methodology
LanguageRefer selects a referred object from scene objects represented by 3D bounding boxes, point clouds, and a language utterance. Its pipeline predicts object classes, combines token and spatial embeddings in a transformer, and trains reference and auxiliary classification and masking tasks.
3 Problem Statement and Methodology
- Given a scene’s objects and an utterance, the task is to predict which object the language refers to.Each object is represented by a 3D bounding box and its enclosed point cloud with xyz and RGB values.Source: §3 · p. 3
- The two-stage pipeline first determines object class labels, then uses spatial-language embeddings to identify the referred object.The model is designed around spatial relationships expressed in language and bounding-box information.Source: §3 · pp. 3–4
- PointNet++ predicts semantic labels from sampled point clouds, and the predicted labels are appended to the utterance before tokenization.PointNet++ achieved 69% average accuracy on the test dataset.Source: §3 · p. 4
- DistilBert embeds the combined utterance and object-label token sequence, while masks identify utterance tokens and each object’s first token.The resulting token embeddings have dimension 768.Source: §3 · p. 4
- Sinusoidal positional encoding transforms each bounding box’s center and size into a vector added to the corresponding object-token embedding before reference classification.The reference model produces output embeddings, and the reference classifier scores object features to select a target.Source: §3 · pp. 4–5
- Training combines reference selection with binary target-class classification and masked-token recovery, which replaces noun tokens with masking noise.At inference, objects outside the predicted target class are filtered using a language-based target-class prediction and top-k semantic-class predictions.Source: Figure 2 · p. 4, §3 · p. 5
4 Experiments
Experiments compare LanguageRefer on ReferIt3D benchmarks and examine label noise, loss choices, and viewpoint correction. Results also probe cross-dataset transfer and view-dependent utterances.
4.1 Datasets; 4.2 Experiment Settings
- Experiments use Nr3D and Sr3D ScanNet scenes, measure candidate-selection accuracy, and compare ReferIt3D methods while separating SAT's extra 2D training data.The evaluation follows the official ScanNet splits; models were initialized from cased DistilBert and trained with AdamW.Source: §4.1 · p. 5, §4.2 · p. 5
4.3 Evaluation on ReferIt3D [2]
- 43.9% on Nr3D (+8.3% over ReferIt3D, +2.2% over the best other model) and 56.0% on Sr3D (+15.2% and +8.0%, respectively) were the strongest results without extra training data.LanguageRefer was comparable to SAT, which used additional 2D image training data.Source: Table 1 · p. 6, §4.3 · p. 6
4.4 Evaluation with Ground-Truth Class Labels
- Cross-dataset evaluation yielded 40.0% on Sr3D and 37.6% on Nr3D; the latter was comparable to non-SAT [40] and InstanceRefer.The authors describe this transfer as evidence of spatial-reasoning generalizability and potential compatibility with different perception modules and datasets.Source: §4.4 · p. 6
- With ground-truth labels for training and evaluation, accuracy reached 54.3% on Nr3D and 91.1% on Sr3D, improving 10.4% and 35.1%, respectively, over noisy-label models.Evaluating noisy-trained models with ground-truth labels increased accuracy by 9.7% on Nr3D and 24.2% on Sr3D.Source: §4.4 · p. 6, Table 2 · p. 7
4.5 Ablation on Loss Terms
- Binary classification loss improved accuracy by +3.5%, +3.9%, and +1.0% across three model comparisons, while mask loss was ineffective and text classification loss degraded accuracy.The selected model omitted text classification loss; the authors hypothesize it may not help transform language embeddings into spatial-language embeddings.Source: §4.5 · p. 7, Table 3 · p. 7
4.6 Viewpoint Annotation
- The authors split view-dependent utterances into explicit- and implicit-viewpoint categories and annotated 12,680 Nr3D utterances against four standard in-room orientations.Annotators selected all orientations considered valid; 5,942 utterances were classified as VD-explicit.Source: §4.6 · pp. 7–8, Figure 3 · p. 8
- Training and evaluation with corrected orientations improved overall accuracy by +5.1%, with +12.7% on view-dependent utterances and +1.4% on view-independent ones.A model trained with correction but evaluated without it was comparable to the final model (−0.4%).Source: §4.6 · p. 8, Table 4 · p. 8
5 Conclusion
LanguageRefer combines utterance and class-label embeddings with position-encoded spatial information for 3D visual grounding. The paper reports performance above state-of-the-art models on ReferIt3D without additional training data and analyzes label noise, viewpoints, and dataset transfer.
5 Conclusion
- LanguageRefer combines language embeddings from utterances and class labels with positional-encoded spatial information to learn a spatial-language embedding space.The model is presented as a modular pipeline for 3D visual grounding.Source: §5 · p. 8