Datasets:

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sequential-3d-grounding

# Sequential 3D Visual Grounding — Technical Documentation / 技术文档

**Version v2 · 2026-09-20**

---

## English

This dataset targets **Sequential 3D Visual Grounding** in indoor scenes.
It covers 5 datasets (ScanNet, HM3D, 3RScan, ARKitScenes, MultiScan), 10301 scenes,
117885 task sequences, and 579872 action steps. Data is converted to 3D-VisTA-compatible format.

### 1. Task Definition

Given a 3D scene (point cloud + instance segmentation) and an **ordered sequence of natural language instructions**,
the model locates the described object at each step. Unlike single-turn grounding, adjacent steps share semantic context —
objects may recur across steps, pronouns may refer to previously mentioned objects.

### 2. Target & Anchor Annotation

| Concept | Definition | Constraint |
|---------|-----------|------------|
| **target** | The object the model must locate in the current step | Exactly 1 per step, never null |
| **anchor** | Reference object(s) that help disambiguate the target | 0, 1, or more; must differ from target |
| **anchor_type** | Source of the anchor | `explicit` (in current sentence) / `historical` (inherited) |

Key rules:
- Navigation steps ("walk to X"): destination X is the target.
- Action steps ("pick up Y on Z"): Y is target, Z is anchor.
- Anchors are extracted from prepositional objects only.
- anchor ≠ target enforced at the instance-id level.

### 3. Data Sources

| Source | Content |
|--------|---------|
| [SceneVerse](https://scene-verse.github.io/) | Unified scanfamily point clouds + instance labels |
| [SG3D](https://github.com/sg-3d/sg3d) | Sequential grounding annotations |
| SceneVerse SSG | Chain-style referring expressions |
| This work | v2 target/anchor redefinition |

All instance labels aligned to ScanNet's 607 categories (verified 100% hit rate).

### 4. Annotation Schema

```json
{
  "scan_id": "ScanNet_scene0350_00",
  "action_steps": [
    {
      "action_id": 1,
      "action": "Walk to the sink underneath the soap dispenser.",
      "target_id": "14",
      "target_object": "sink",
      "target_changed": true,
      "anchors": [
        {"anchor_id": "24", "anchor_object": "soap dispenser", "anchor_type": "explicit"}
      ],
      "anchor_source": "rule_backfill"
    }
  ]
}
```

### 5. Statistics

| Dataset | Train | Val | Test |
|---------|------:|-----:|-----:|
| ScanNet | 14501 | 1700 | 1808 |
| HM3D | 33302 | 7663 | 4232 |
| 3RScan | 13826 | 4864 | 1937 |
| ARKitScenes | 24349 | 1394 | 2653 |
| MultiScan | 4133 | 858 | 665 |
| **Total** | **90111** | **16479** | **11295** |

Anchor distribution (per step): 0 = 1.8%, 1 = 36.0%, ≥2 = 62.2%.

### 6. Quality Control

| Check | Result |
|-------|--------|
| target_id missing | 104 (0.018%, 3RScan unresolvable) |
| target_id out of range | 0 |
| anchor_id out of range | 0 |
| anchor == target | 0 |
| anchor-target distance (median) | 0.32× scene scale |

### 7. Usage

1. Point 3D-VisTA's `SCAN_FAMILY_BASE` to `scan_data/`.
2. Load `annotations/dynamic_data/{dataset}_{split}.json`.
3. Strip `{Dataset}_` prefix from `scan_id` to get the scene name.
4. Add an anchor head alongside the existing grounding head; train with multi-task loss `L = L_target + λ·L_anchor`.

### 8. Scripts

| Script | Function |
|--------|----------|
| `scanfamily_to_3dvista.py` | Point cloud format conversion |
| `resolve_target_anchors.py` | LLM names → instance IDs |
| `build_dynamic_sequences.py` | SSG chain windowing + assembly |
| `filtered_to_v2.py` | Legacy data → v2 schema |
| `backfill_filtered_anchors.py` | Backfill missing prepositional anchors |
| `llm_augment_anchors.py` | Spatially-verified LLM augmentation (train only) |
| `generate_unified_splits.py` | Scene-level splits |
| `final_qc.py` | Exhaustive quality check |

---

## 中文

本数据集面向 3D 室内场景中的**连续指令定位**（Sequential 3D Visual Grounding）研究。
覆盖 ScanNet、HM3D、3RScan、ARKitScenes、MultiScan 共 5 个数据集、10301 个场景，
117885 个任务序列、579872 个动作步。

### 1. 任务定义

给定 3D 场景（点云 + 实例分割）和一组**有序自然语言指令序列**，
模型在每一步定位该步所描述的物体。与单轮 grounding 的区别：相邻步之间存在语义关联，
同一物体可能跨步复现，代词可能指代前序步骤中已出现的对象。

### 2. Target 与 Anchor 标注

| 概念 | 定义 | 约束 |
|---|---|---|
| **target** | 当前步模型必须定位的物体 | 每步恰好 1 个，不缺失 |
| **anchor** | 帮助确定 target 位置或消歧的参照物体 | 0、1 或多个；与 target 不同 |
| **anchor_type** | anchor 来源 | `explicit`（当前句）/ `historical`（跨步继承） |

判定规则：
- 导航步（walk to X）：目的地 X 为 target。
- 操作步（pick up Y on Z）：Y 为 target，Z 为 anchor。
- anchor 仅取介词宾语，排除非空间参照的普通名词。
- id 层强制 anchor ≠ target。

### 3. 数据来源

| 来源 | 内容 |
|---|---|
| [SceneVerse](https://scene-verse.github.io/) | 五数据集统一点云与实例标签 |
| [SG3D](https://github.com/sg-3d/sg3d) | 序列化 grounding 标注 |
| SceneVerse SSG | chain 式指代表达 |
| 本数据集 | v2 target/anchor 重定义 |

所有实例标签对齐 ScanNet 607 类（逐数据集验证 100% 命中）。

### 4. 标注格式

```json
{
  "scan_id": "ScanNet_scene0350_00",
  "action_steps": [
    {
      "action_id": 1,
      "action": "Walk to the sink underneath the soap dispenser.",
      "target_id": "14",
      "target_object": "sink",
      "target_changed": true,
      "anchors": [
        {"anchor_id": "24", "anchor_object": "soap dispenser", "anchor_type": "explicit"}
      ],
      "anchor_source": "rule_backfill"
    }
  ]
}
```

### 5. 统计

| 数据集 | Train | Val | Test |
|---|---:|---:|---:|
| ScanNet | 14501 | 1700 | 1808 |
| HM3D | 33302 | 7663 | 4232 |
| 3RScan | 13826 | 4864 | 1937 |
| ARKitScenes | 24349 | 1394 | 2653 |
| MultiScan | 4133 | 858 | 665 |
| **合计** | **90111** | **16479** | **11295** |

Anchor 分布（每步）：0 个 = 1.8%，1 个 = 36.0%，≥2 个 = 62.2%。

### 6. 质检

| 检查项 | 结果 |
|---|---|
| target_id 缺失 | 104 (0.018%，3RScan 无法解析) |
| target_id 越界 | 0 |
| anchor_id 越界 | 0 |
| anchor == target | 0 |
| anchor-target 距离中位数 | 0.32 倍场景尺度 |

### 7. 使用方法

1. 将 3D-VisTA 的 `SCAN_FAMILY_BASE` 指向 `scan_data/`。
2. 加载 `annotations/dynamic_data/{dataset}_{split}.json`。
3. `scan_id` 去掉 `{Dataset}_` 前缀得到场景名。
4. 在现有 grounding head 旁增加 anchor head，多任务损失 `L = L_target + λ·L_anchor`。

### 8. 脚本

| 脚本 | 功能 |
|---|---|
| `scanfamily_to_3dvista.py` | 点云格式转换 |
| `resolve_target_anchors.py` | LLM 名词 → 实例 id |
| `build_dynamic_sequences.py` | SSG chains 切窗 + 组装 |
| `filtered_to_v2.py` | 旧标注转 v2 |
| `backfill_filtered_anchors.py` | 介词参照漏标补齐 |
| `llm_augment_anchors.py` | 空间校验 LLM 增强（仅 train） |
| `generate_unified_splits.py` | 场景级切分 |
| `final_qc.py` | 全面质检 |

---

*Machine-readable QC report: [`final_qc_report.json`](final_qc_report.json)*