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arxiv: 2606.20946 · v1 · pith:PCPZQQEEnew · submitted 2026-06-18 · 💻 cs.CL · cs.CV

Scaling Diverse Language Generation for 3D Visual Grounding

Austin T. Wang , Dongchen Yang , Angel X. Chang This is my paper

Pith reviewed 2026-06-26 16:56 UTC · model grok-4.3

classification 💻 cs.CL cs.CV
keywords 3D visual groundingscene graphslarge language modelsdiverse data generationvision language modelsspatial reasoning3DVGlanguage diversity
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The pith

A scalable method using scene graphs and LLMs generates diverse 3D visual grounding queries that improve model performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Current 3D visual grounding models struggle because existing datasets lack variety in how objects are described in natural language. The paper introduces ViGiL3D++, which samples different types of constraints from 3D scene graphs and has large language models convert those into sentences. This produces a larger and more varied set of training examples than previous approaches. When models are trained with this data, they perform better on standard 3DVG test sets. The results also point to specific areas where vision-language models still fall short on complex spatial references.

Core claim

ViGiL3D++ is a scene-agnostic pipeline that samples constraint types from scene graphs and uses LLMs to generate natural language queries realizing those constraints. The resulting data shows greater diversity in language and constraint types than prior scaled datasets. Models trained on it achieve higher performance across several 3D visual grounding benchmarks, yet the evaluation also reveals ongoing limitations in how well VLMs handle the generated queries.

What carries the argument

ViGiL3D++, which samples scene-graph constraints and prompts LLMs to produce corresponding natural language descriptions for visual grounding.

If this is right

  • Training on the generated queries leads to better generalization on 3DVG benchmarks.
  • The dataset enables evaluation of models on a wider range of linguistic patterns and constraint combinations.
  • It provides a way to scale up data without relying on human annotation for every description.
  • The method highlights that VLMs have trouble with certain types of complex grounding even when trained on diverse data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach might transfer to generating data for other tasks involving spatial language and 3D scenes, such as navigation or manipulation.
  • Biases or hallucinations from the LLMs could affect the quality of the data in ways not fully captured by the diversity metrics.
  • Future work could combine this generation with verification mechanisms to reduce semantic drift.
  • This could help in creating more robust benchmarks that better test compositional understanding in grounding models.

Load-bearing premise

LLM outputs will faithfully express only the sampled scene graph constraints without adding unintended details or errors.

What would settle it

If a large sample of the generated queries is manually verified against their source scene graphs and many are found to contain mismatches or extra information, the performance improvements could be attributed to noise rather than true diversity gains.

Figures

Figures reproduced from arXiv: 2606.20946 by Angel X. Chang, Austin T. Wang, Dongchen Yang.

Figure 1
Figure 1. Figure 1: Comparison of 3D query generation methods. Left: To scale datasets for visual grounding (VG), recent work automatically generate VG descriptions (queries) for objects in 3D scene datasets using templates or direct MLLM-based generation from (a) images, (b) captions, or (c) scene graphs. Their queries lack linguistic diversity and fail to synthesize information across the entire scene into cohesive sentence… view at source ↗
Figure 2
Figure 2. Figure 2: Grounding annotation pipeline. We present our pipeline for sampling grounding descrip￾tions from 3D scenes. 1) We extract a cross-referencing scene graph by parsing attributes using a VLM from multi-view images of each object and relationships through geometric analysis. 2) For each query, we sample targets conditioned on the query type (zero-, single-, or multi-target) and label. Constraints are iterative… view at source ↗
Figure 3
Figure 3. Figure 3: Scene graph extraction pipeline. We extract the scene graph from the point cloud and RGB images. We first initialize the objects from ground truth or predicted segmentation. We then add attributes and relationships to the graph. 1) To extract attributes, (a) we first augment the initial object labels with additional object classes. (b) We then identify identical objects, and subsequently we perform (c) att… view at source ↗
Figure 4
Figure 4. Figure 4: Model architecture. Given a grounding description and a point cloud, the point cloud is first segmented, using GT during training and either GT or Mask3D (Schult et al., 2023) predictions during inference, and then processed using PointNet++ (Qi et al., 2017) to extract point features per object. We then add explicit pairwise spatial information to output object features f O i . For text, we use a BERT-lik… view at source ↗
Figure 5
Figure 5. Figure 5: Scene graph visualization. We visualize a subgraph of the scene graph for the following scene, including a sample of the attributes and relationships extracted for each. (a) Semantic Input Wall Free Space (b) Occupancy Grid Wall (dilated) Free Space Unknown Room 1 Room 2 Room 3 Room 4 Room 5 Room 6 (c) Room Segmentation [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Layout estimation pipeline. (a) Semantic annotations from the 3D mesh are projected onto the XY plane to identify walls and free space (floor and objects). (b) A 2D occupancy grid is constructed at 5 cm resolution, where walls are dilated using morphological operations to close gaps at doorways and thin partitions. (c) Connected component labeling on the free space regions segments the scene into individua… view at source ↗
Figure 7
Figure 7. Figure 7: Identifying similar objects. To ensure consistent assignment of attributes to objects, we first identify all groups of objects that have the same attributes, thus ensuring that attributes are assigned consistently among identical objects. 1) The two chairs share all of the same attributes, including color, whereas 2) the dark chair has a different color and thus is assigned attributes separately. 22 [PITH… view at source ↗
Figure 8
Figure 8. Figure 8: Object counts. Number of target objects (left) and anchor objects (right) per VG query in ViGiL3D++. object thingwall furniture device storage chair container appliance table door window cabinet shelf seating trash can pillow box lamp picture Target Name 0 200 400 600 800 1000 Frequency Top 20 Target Names across ViGiL3D++ [PITH_FULL_IMAGE:figures/full_fig_p032_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Target object word cloud. Frequency of target object referents in ViGiL3D++. The most frequent target labels are more coarse-grained, making the queries more challenging and requiring models to not only rely on unique object names for grounding. must be used by the model to identify the target object. 3) For zero-target queries, we use attributes and relationships describing objects of the target class, ra… view at source ↗
Figure 10
Figure 10. Figure 10: Query complexity. Number of sampled constraints (left) and number of words (right) per VG query in ViGiL3D++. Zero Single Multi Total VLM Unique Common Total GPT-4.1 (2023) 78.6 (9.6) 100.0 (0.0) 43.1 (12.0) 47.1 (11.7) 45.7 (11.7) 57.1 (6.7) InternVL (2025) 50.0 (13.9) 75.0(30.0) 23.8(12.9) 32.0 (12.9) 30.0 (12.7) 37.3(7.7) Qwen3-VL (2025) 72.0 (12.4) 83.3(21.1) 36.8(15.3) 48.0 (13.8) 36.0 (13.3) 52.0(8.… view at source ↗
Figure 11
Figure 11. Figure 11: Failure cases for prior methods. Previous methods most often fail to distinguish the target object from distractors. Qwen3-VL-30B-A3B-Instruct-FP8 (Qwen Team, 2025), to investigate the compara￾tive performance of alternative models. We provide the validity results in [PITH_FULL_IMAGE:figures/full_fig_p034_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Failure cases for ViGiL3D++. Common validity failures include incorrect attributes or relationships, imperfect point cloud reconstructions, and contextual relationships. 38 [PITH_FULL_IMAGE:figures/full_fig_p038_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: User study interface. Users are presented with the 3D point cloud of the scene (center) and a grounding description (bottom) along with the localization of the target(s). The proposed targets are highlighted in the bounding box (green). If the targets are exactly the objects described by the query, then the user selects “True”. Otherwise, the user selects “False”. GT ZSVG3D 3D-VisTA 3D￾GRAND GPS V3DM A bl… view at source ↗
Figure 14
Figure 14. Figure 14: Examples. V3DM trained on ViGiL3D++ is able to predict correctly (green) on challeng￾ing zero-, single-, and multi-target queries from ViGiL3D over existing methods, which often identify the wrong objects (red). 39 [PITH_FULL_IMAGE:figures/full_fig_p039_14.png] view at source ↗
read the original abstract

Developing robust models for 3D visual grounding (3DVG), the localization of entities in a 3D scene described in natural language, is important for enabling agents to correspond spatial language with objects in the physical world. However, the lack of diverse descriptions at scale prevents models from generalizing beyond simple linguistic patterns. Recent such attempts lack diversity in the constraint types and language used to ground objects. Captioning methods cannot precisely contrast objects, which is important for visual grounding. We therefore propose ViGiL3D++, a scalable, scene-agnostic method that generates diverse visual grounding queries by combining constraint sampling in scene graphs with the language generation of LLMs. We show that it has greater diversity over existing scaled datasets and improves model performance over several 3DVG benchmarks but also illuminates outstanding limitations of VLMs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper proposes ViGiL3D++, a scalable scene-agnostic method for generating diverse natural-language queries for 3D visual grounding. Constraints are sampled from scene graphs and fed to LLMs to produce sentences; the authors claim this yields greater diversity than existing scaled datasets, improves performance on multiple 3DVG benchmarks, and reveals limitations of current VLMs.

Significance. If the generated queries are shown to be faithful realizations of the sampled constraints, the approach would address a documented gap in 3DVG training data by enabling controlled diversity at scale, moving beyond the limitations of captioning methods that cannot precisely contrast objects. This could support better generalization in spatial language grounding tasks.

major comments (1)
  1. [Method / generation procedure] Method / generation procedure: The central claims of greater diversity and benchmark gains rest on the premise that LLM-generated sentences faithfully realize exactly the sampled scene-graph constraints (no extra relations, no dropped constraints, no object mis-identifications). No quantitative check—constraint-adherence rate, human verification of a held-out sample, or direct comparison of generated text against the original graph—is reported. This verification is load-bearing; without it, diversity metrics and performance improvements cannot be attributed to controlled constraint coverage rather than LLM style, data volume, or noise.
minor comments (1)
  1. [Abstract] Abstract: The abstract states diversity gains and benchmark improvements but supplies no numerical values, diversity measures, or ablation details, forcing readers to consult later sections for any assessment of the claims.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback emphasizing the need to verify constraint fidelity. We address the major comment below.

read point-by-point responses

  1. Referee: The central claims of greater diversity and benchmark gains rest on the premise that LLM-generated sentences faithfully realize exactly the sampled scene-graph constraints (no extra relations, no dropped constraints, no object mis-identifications). No quantitative check—constraint-adherence rate, human verification of a held-out sample, or direct comparison of generated text against the original graph—is reported. This verification is load-bearing; without it, diversity metrics and performance improvements cannot be attributed to controlled constraint coverage rather than LLM style, data volume, or noise.

    Authors: We agree that explicit verification of constraint adherence is necessary to attribute gains specifically to controlled coverage. Our pipeline samples constraints from scene graphs and prompts the LLM with instructions to realize exactly those constraints in the output sentence. However, the manuscript does not include a quantitative adherence rate, human verification on a held-out sample, or automated comparison of generated text to the input graph. We will add this analysis in revision, reporting both automated parsing where feasible and human evaluation of adherence on a sample of generations. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical generation procedure

full rationale

The paper presents an empirical method that samples constraints from scene graphs and prompts LLMs to generate natural-language queries for 3D visual grounding. Diversity and benchmark gains are reported via direct measurement on external datasets and models. No mathematical derivations, equations, fitted parameters, or predictions are claimed that could reduce to self-referential inputs by construction. The central construction relies on independent external components (scene graphs, LLMs) rather than any self-definition or load-bearing self-citation chain. This is the expected non-finding for a purely empirical scaling paper.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach depends on the fidelity of scene graphs as input and on LLMs producing constraint-faithful language; no free parameters or new entities are introduced in the abstract.

axioms (2)
  • domain assumption Scene graphs provide a complete and accurate basis for sampling contrastive constraints in 3D scenes.
    The generation method begins with constraint sampling in scene graphs.
  • domain assumption LLMs can reliably translate sampled constraints into diverse, accurate natural language without semantic alteration.
    Language generation step relies on LLM output matching the input constraints.

pith-pipeline@v0.9.1-grok · 5668 in / 1119 out tokens · 22047 ms · 2026-06-26T16:56:48.772804+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

31 extracted references · 4 canonical work pages · 1 internal anchor

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    URLhttps://arxiv.org/abs/2309.05251. Zhuofan Zhang, Ziyu Zhu, Pengxiang Li, Tengyu Liu, Xiaojian Ma, Yixin Chen, Baoxiong Jia, Siyuan Huang, and Qing Li. Task-oriented sequential grounding in 3D scenes.arXiv preprint arXiv:2408.04034, 2024b. URLhttps://arxiv.org/abs/2408.04034. Jiahe Zhao, Ruibing Hou, Zejie Tian, Hong Chang, and Shiguang Shan. HIS-GPT: T...

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    |", and no other text. The descriptions should vary in content and style

    There is a row of black rolling chairs along the wall. Pick the one furthest to the left when facing them. You should return {num_candidates} candidate descriptions, separated by "|", and no other text. The descriptions should vary in content and style. """ Listing 1:Im2Query Prompt.We provide a list of annotated multiple views and the target name and ask...

  6. [15]

    Of such boxes, it is the one not next to an armchair

    This is a cardboard box against the wall and right next to two doors. Of such boxes, it is the one not next to an armchair

  7. [16]

    On the brown, wooden table is a small, rectangular projector

  8. [17]

    This is the larger of the two toolboxes near the piano

  9. [18]

    Find the object with wheels

  10. [19]

    When facing the radiator, the metal rail is directly on your left

  11. [20]

    To the left of the sink is a set of paper towel rolls

  12. [21]

    This circular object is supported by a white mini fridge

  13. [22]

    Underneath the sink is a cabinet for storing cleaning supplies

  14. [23]

    Locate the tallest appliance in the kitchen

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    |", and no other text. The descriptions should vary in content and style

    There is a row of black rolling chairs along the wall. Pick the one furthest to the left when facing them. You should return {num_candidates} candidate descriptions, separated by "|", and no other text. The descriptions should vary in content and style. """ Listing 2:Cap2Query Prompt.We provide a list of annotated multiple views for each object and ask th...

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    on" (inverse:

    "on" (inverse: "s u p p o r t s"):if the subject object is physically resting on top of the recipient object (e.g . a book on a table, a bottle on a shelf)

  17. [26]

    i n" (inverse:

    "i n" (inverse: "c o n t a i n s"): if the subject object is physically inside, or embedded inside, the recipient object (e.g. a book in a box, a cup in a cupboard, door in a wall)

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    embeddedi n

    "embeddedi n" (inverse: "embeds"): if the subject object is partially inside the recipient object, such that part of the subject is accessible at the surface of the recipient (e.g. a stove embedded in the counter, a sink embedded in the counter)

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    l e a n i n g on

    "l e a n i n g on" (inverse: "s u p p o r t s"):if the subject object is leaning against the recipient object, such that if you were to remove the recipient, the subject would obviously fall. Horizontal contact of one object against another on its own does not count as leaning (e.g. a ladder leaning on a wall, an instrument case leaning against a table)

  20. [29]

    h a n g i n g on

    "h a n g i n g on" (inverse: "s u p p o r t s"):if the subject object is hanging from the recipient object (e.g. a towel hanging on a rack, a lamp hanging from the ceiling, a picture hanging on the wall)

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    none" (inverse:

    "none" (inverse: "none"): if none of the above relationships apply. # How to Determine the Relationship You will be provided the IDs and labels of the objects. The objects of interest should be marked with blue ( subject) and orange (recipient) outlines around the objects, and ignore the highlighted red area. You should determine the appropriate relations...

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    on" the shelf and not

    How are the objects typically described? (e.g. if a book is supported by a shelf, the book is usually described as "on" the shelf and not "i n" the shelf)

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    none") # Output Format The output should be a JSON object with the following format: {

    Are the objects in the scene actually exhibiting the relationship? (e.g. if the book is actually only next to the table, then the relationship is "none") # Output Format The output should be a JSON object with the following format: { "s u b j e c t _ t o _ r e c i p i e n t":one of ["s u p p o r t s","on", "c o n t a i n s", "i n", "l e a n i n g on", "h ...

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    a", "an",

    or by prompting a VLM with the rendered image (Wang et al., 2024b; Yang et al., 2024b). Object attributes are typically obtained from a combination of ground-truth annotations (e.g. semantic class of the object), 3D reasoning (e.g. using the size of the bounding box to estimate the size of the object), and appearance attributes (e.g. color, style) from a ...

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    Go to <URL> to view the scene assignments

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    In the scene viewer, select the corresponding scene ID from the left bar

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    The object should be marked with a green bounding box in the visualizer

    Click on an object in the``Objects'' tab on the right to pull up all of the descriptions of that object in the scene. The object should be marked with a green bounding box in the visualizer

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    For multi-target prompts, click on``Multi-Target Prompts'' on the right side and then click on each prompt to see its corresponding targets

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    For zero-target prompts, no objects will be marked in the scene

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    For each description, select``True'' if the prompt correctly describes every annotated target (and no other segmented objects in the scene), or``False'' otherwise

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    True”. Otherwise, the user selects “False

    Export and upload your annotation at <URL>. Listing 11:User study instructions. Unique Multiple Overall Trained Acc@25 Acc@50 Acc@25 Acc@50 Acc@25 Acc@50 3D-VisTA ScanScribe 46.1 43.6 20.3 18.4 25.3 23.3 GPS SceneVerse50.1 46.819.9 17.7 25.8 23.4 V3DM ViGiL3D++ 44.0 41.3 17.4 15.6 22.5 20.6 V3DM ViGiL3D++ SR 48.0 45.3 22.8 20.8 27.7 25.5 Table 14: Accurac...

    2023