---
格式版本: 2
标题: "CausalSplat: Towards Comprehensive Hierarchical Reasoning in 3D Gaussian Splatting"
原文链接: "https://arxiv.org/abs/2608.11150"
发布日期: "2026-08-11"
发布时间校准状态: "found"
发布时间需复核: "否"
发布时间来源: "rule:local:strict_original_body"
发布时间证据: "**\\[v1\\]** Tue, 11 Aug 2026 17:09:49 UTC (5,518 KB)"
发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body"
发布时间校准置信度: "high"
发布时间候选数量: 18
发布时间严格候选数量: 6
发布时间原页读取状态: "source template page reused from URL open"
发布时间未找到原因: ""
发布时间校准时间: "2026-08-12T11:18:15+08:00"
发布时间仲裁状态: "skipped"
发布时间仲裁尝试次数: 0
发布时间仲裁耗时毫秒: 0
发现时间: "2026-08-12T11:16:29+08:00"
入库时间: "2026-08-12T03:18:15.368Z"
来源平台: "arXiv 学术论文搜索"
搜索渠道: "source_template"
搜索词: "https://arxiv.org/search/?query=performance&searchtype=all"
匹配关键词:
  - "performance"
相关厂家:
  []
相关专家:
  []
内容类型: "网页"
抓取工具: "Free Fetch + Defuddle"
清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取"
原始附件:
  []
AI优质: "否"
AI打分: 15
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "论文内容为3D视觉推理，与超节点/AI机柜/供电散热互连等核心主题完全无关。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T11:18:27+08:00"
AI主题相关性: 0
AI来源权威性: 5
AI新颖性: 0
AI技术细节: 0
AI商业部署信号: 0
AI完整性: 10
采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.11150"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:CausalSplat: Towards Comprehensive Hierarchical Reasoning in 3D Gaussian Splatting

Authors:[Jiayu Ding](https://arxiv.org/search/cs?searchtype=author&query=Ding,+J), [Meilu Song](https://arxiv.org/search/cs?searchtype=author&query=Song,+M), [Yun Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+Y), [Wei Gao](https://arxiv.org/search/cs?searchtype=author&query=Gao,+W), [Ge Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+G)

[View PDF](https://arxiv.org/pdf/2608.11150) [HTML (experimental)](https://arxiv.org/html/2608.11150v1)

> Abstract:While 3D Gaussian Splatting (3DGS) has advanced open vocabulary scene understanding, existing methods remain confined to explicit queries. They struggle to interpret implicit intents, complex spatial constraints, and commonsense reasoning required for practical embodied interactions. To address this gap, we introduce the task of reasoning 3D Gaussian segmentation and construct two benchmarks, Causal-LERF and Causal-ScanNet. These benchmarks systematically evaluate commonsense, spatial, affordance, and counterfactual reasoning. Evaluations reveal that current state of the art methods perform poorly on these reasoning challenges. Therefore, we propose CausalSplat, a framework that integrates vision-language models with 3D scene graphs to disentangle explicit structural perception from implicit logical inference. Extensive experiments demonstrate that CausalSplat achieves state of the art performance on our reasoning benchmarks while showing strong generalizability on standard referring and open vocabulary 3D segmentation tasks. Project Page: [this https URL](https://jiayuding031020.github.io/CausalSplat)

| Comments: |  |
| --- | --- |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | [arXiv:2608.11150](https://arxiv.org/abs/2608.11150) \[cs.CV\] |
|  | (or [arXiv:2608.11150v1](https://arxiv.org/abs/2608.11150v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11150](https://doi.org/10.48550/arXiv.2608.11150) |

## Submission history

From: Jiayu Ding \[[view email](https://arxiv.org/show-email/49584839/2608.11150)\]  
**\[v1\]** Tue, 11 Aug 2026 17:09:49 UTC (5,518 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11150) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
