OpenScan: A Benchmark for Generalized Open-Vocabulary 3D Scene Understanding
Abstract: Open-vocabulary 3D scene understanding (OV-3D) aims to localize and classify novel objects beyond the closed set of object classes. However, existing approaches and benchmarks primarily focus on the open vocabulary problem within the context of object classes, which is insufficient in providing a holistic evaluation to what extent a model understands the 3D scene. In this paper, we introduce a more challenging task called Generalized Open-Vocabulary 3D Scene Understanding (GOV-3D) to explore the open vocabulary problem beyond object classes. It encompasses an open and diverse set of generalized knowledge, expressed as linguistic queries of fine-grained and object-specific attributes. To this end, we contribute a new benchmark named \textit{OpenScan}, which consists of 3D object attributes across eight representative linguistic aspects, including affordance, property, and material. We further evaluate state-of-the-art OV-3D methods on our OpenScan benchmark and discover that these methods struggle to comprehend the abstract vocabularies of the GOV-3D task, a challenge that cannot be addressed simply by scaling up object classes during training. We highlight the limitations of existing methodologies and explore promising directions to overcome the identified shortcomings.
Summary
- The paper introduces a novel GOV-3D task that extends object classification to include eight linguistic attribute aspects.
- The methodology leverages ConceptNet-derived associations combined with rigorous manual annotation to ensure semantic consistency.
- Experimental results reveal that state-of-the-art models struggle with abstract attributes, emphasizing the need for integrated linguistic cues.
OpenScan: A Benchmark for Generalized Open-Vocabulary 3D Scene Understanding
Introduction
The field of open-vocabulary 3D scene understanding (OV-3D) has primarily revolved around identifying and classifying object classes that extend beyond the pre-defined categories present in training datasets. This task has gained relevance in various applications such as autonomous driving and robot navigation. Vision-LLMs (VLMs), which have lately witnessed substantial breakthroughs due to their ability to leverage large-scale paired image-text datasets, have breathed new life into the open-vocabulary paradigm. However, existing benchmarks for OV-3D predominantly focus on object classes without thoroughly evaluating models' abilities to comprehend additional attributes that define these objects.
Emerging from this necessity is the concept of Generalized Open-Vocabulary 3D Scene Understanding (GOV-3D), a paradigm that transcends traditional object classification. Instead, GOV-3D encompasses a diverse range of object-related attributes expressed as linguistic queries, facilitating a more holistic assessment of 3D scene comprehension capabilities. The introduction of the OpenScan benchmark serves as a critical step in exploring GOV-3D. OpenScan introduces linguistic queries encompassing eight representative aspects: affordance, property, type, manner, synonym, requirement, element, and material.
Figure 1: The proposed Generalized Open-Vocabulary 3D Scene Understanding (GOV-3D) task expands the vocabulary types of the classic 3D Scene Understanding (OV-3D) task.
Benchmark Description
OpenScan is meticulously designed to overcome limitations observed in pre-existing OV-3D benchmarks such as ScanNet and ScanNet200, which are largely focused on object classes without encompassing broader attribute vocabularies. OpenScan builds upon these previous datasets by extending the annotations to include object-related attributes across varied linguistic aspects. This enables models to be evaluated more comprehensively in terms of their understanding and generalization capabilities.
Figure 2: OpenScan benchmark samples. The target objects are highlighted in blue.
To annotate OpenScan, associations between objects and attributes are derived from ConceptNet, a high-quality NLP knowledge base. The benchmark then undergoes rigorous manual annotation to ensure visual attributes are accurately represented. The attributes are classified into linguistic aspects ensuring semantic consistency across the dataset. This categorization facilitates a structured evaluation approach, allowing for targeted assessments of OV-3D models across different attribute types.
Experimental Evaluation
A suite of state-of-the-art OV-3D models has been evaluated on the newly constructed OpenScan benchmark. Experimental results indicate that while these models perform robustly on traditional object class prediction tasks, they exhibit substantial difficulty in accurately predicting object-specific attributes. Models such as OpenMask3D demonstrate competency in segmentation tasks, particularly where strong visual cues (e.g., material attributes) contribute to prediction accuracy, but struggle with more abstract requirements (e.g., affordance).
Figure 3: Impact of different pre-training vocabulary size.
The analysis highlights that simply scaling the vocabulary size during training does not translate linearly to improved performance on GOV-3D tasks. Instead, models benefit from the integration of linguistic templates that better contextualize the attributes within the 3D scenes. Additionally, leveraging knowledge graphs and LLMs to convert attribute queries back to class queries presents a promising avenue for enhancing attribute comprehension.
Figure 4: Visualization of the Open3DIS failure case with the corresponding CLIP image-text similarity scores.
Conclusions
The OpenScan benchmark unequivocally demonstrates the shortcomings of current OV-3D models in generalizing beyond predefined object classes to a wide array of linguistic attributes. This delineates critical areas for improvement, notably in the field of commonsense reasoning and attribute understanding.
As GOV-3D tasks necessitate models to link linguistic queries with complex object attributes, future research stands to benefit from benchmark expansion, incorporation of diverse 3D datasets, and enhancements to training regimes that emphasize attribute-based comprehension. The introduction and utilization of OpenScan will undoubtedly catalyze advancements in the field, ensuring that future models boast robust understanding capacities that traverse conventional boundaries.
Figure 5: Radar charts of AP, AP, and AP results for eight linguistic aspects on our OpenScan benchmark.
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What this paper is about (in simple terms)
Imagine a robot looking around a room made of 3D dots (a “point cloud”), like in a video game world. Many AI systems can find “a chair” or “a table” if you give them those exact words. But real people also talk about things in other ways, like “something you sit on” or “a bottle made of plastic.” This paper introduces a new way to test AI so it can understand both object names and richer descriptions about objects.
The authors build a new test set, called OpenScan, and a harder task, called Generalized Open‑Vocabulary 3D Scene Understanding (GOV‑3D). This task asks AI to find 3D objects not just by their names, but also by attributes like what they’re used for, what they’re made of, or how they’re described in everyday language.
The main goals and questions
The paper asks:
- Can today’s AI models find objects in 3D scenes when we describe them using everyday attributes instead of just names?
- How well do these models handle different kinds of attributes (like “made of wood,” “used for sleeping,” or “also called a bedside table”)?
- What helps these models do better: training on more object names, or writing clearer queries that include relationships (like “this thing is made of wood”)?
How they approached it (with easy explanations)
To test AI fairly, they needed a benchmark (a big, well-labeled dataset + rules for evaluation). They built OpenScan on top of an existing 3D dataset (ScanNet200), which already had 3D scans of indoor scenes and object labels (e.g., bed, fridge).
What they added:
- Extra attribute labels for objects across eight language-based aspects. You can think of these as different ways we talk about objects:
- Affordance (what it’s used for): “sit” (for chair)
- Property (what it’s like): “soft” (for pillow)
- Type (what group it belongs to): “communication device” (for telephone)
- Manner (how it’s used/worn): “worn on the head” (for hat)
- Synonym (another word for it): “bedside table” (for nightstand)
- Requirement (what it needs): “balance to ride” (for bicycle)
- Element (its parts): “two wheels” (for bicycle)
- Material (what it’s made of): “plastic” (for bottle)
How they got these attributes:
- They used a knowledge graph (ConceptNet), which is like a giant dictionary of facts that connects words and ideas (e.g., “bed — is used for — sleep”).
- They also had people add visual-only attributes when needed (like material), because some things you can only tell by looking.
- They cleaned and grouped these attributes into the eight aspects above, made sure they were consistent, and removed duplicates.
How they asked questions to the AI:
- Instead of giving away the object name, they wrote queries like “this thing is used for sleeping,” or “this thing is made of wood.” That way, the AI has to match the attribute to the right 3D object.
How they tested models:
- They took several state-of-the-art 3D models that already work with text (often powered by image-text models like CLIP).
- They evaluated them in a “zero-shot” way (no extra training on OpenScan), asking them to find objects by attributes across many scenes.
- They compared performance on classic “object name” tests vs. the new “attribute” tests.
What they found and why it matters
Main findings:
- Today’s best models are good at finding objects when you use their names (“chair,” “fridge”), but their performance drops a lot when you use attributes instead (“something you sit on,” “keeps food cold”).
- Models do a bit better on:
- Synonyms (because they’re very close to object names).
- Material (e.g., “wood,” “plastic”), likely because visual patterns like texture and color are easier to pick up from images.
- Models struggle more with:
- Affordance (what it’s used for) and
- Property (characteristics like “soft”), which are more abstract and need commonsense understanding.
- Simply training on more object names doesn’t fix this. Adding more categories (like going from 150 to 200 object labels) doesn’t significantly improve attribute understanding.
- Writing clearer queries helps. Using a template like “this thing is made of wood” works better than just “wood,” because it gives helpful context.
Why this matters:
- In real-life settings—like home robots, AR devices, or self-driving systems—people won’t always use exact object names. They’ll describe what things do or what they’re like. For AI to be truly helpful, it needs to understand those natural descriptions.
- The big gap in performance shows that current models are missing important commonsense knowledge and deeper language understanding about objects.
What this means for the future
- OpenScan gives the community a large, carefully built benchmark to push AI from recognizing object names to understanding richer, everyday descriptions.
- The results suggest new research directions:
- Teach models more attribute and commonsense knowledge (not just more object names).
- Improve the way language is connected to 3D vision, especially for abstract attributes.
- Use better query designs and training methods that focus on relationships (e.g., “is used for,” “is made of”) rather than just labels.
In short, this work raises the bar: it encourages building AI that not only knows “what a thing is called” but also “what it does,” “what it’s like,” and “what it’s made of”—much closer to how people think and talk.
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