Ilov3Splat: Instance-Level Open-Vocabulary 3D Scene Understanding in Gaussian Splatting

• URL: http://arxiv.org/abs/2605.04506v2
• Date: Wed, 13 May 2026 00:18:56 GMT
• Title: Ilov3Splat: Instance-Level Open-Vocabulary 3D Scene Understanding in Gaussian Splatting
• Abstract summary: Ilov3Splat is a framework for instance-level open-vocabulary 3D scene understanding built on 3D Gaussian Splatting (3D-GS)<n>We leverage multi-resolution hash embedding to efficiently encode language-aligned CLIP features.<n>At inference time, CLIP-encoded queries are matched against the learned features, followed by two-stage 3D clustering to retrieve relevant Gaussian groups.
• Score: 32.46353940664006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Ilov3Splat, a novel framework for instance-level open-vocabulary 3D scene understanding built on 3D Gaussian Splatting (3D-GS). Most prior work depends on 2D rendering-based matching or point-level semantic association, which undermines cross-view consistency, lacks coherent instance-level reasoning, and limits precision in downstream 3D tasks. To address these limitations, our method jointly optimizes scene geometry and semantic representations by augmenting Gaussian splats with view-consistent feature fields. Specifically, we leverage multi-resolution hash embedding to efficiently encode language-aligned CLIP features, enabling dense and coherent language grounding in 3D space. We further train an instance feature field using contrastive loss over SAM masks, supporting fine-grained object distinction across views. At inference time, CLIP-encoded queries are matched against the learned features, followed by two-stage 3D clustering to retrieve relevant Gaussian groups. This enables our framework to identify arbitrary objects in 3D scenes based on natural language descriptions, without requiring category supervision or manual annotations. Experiments on standard benchmarks demonstrate that Ilov3Splat outperforms prior open-vocabulary 3D-GS methods in both object selection and instance segmentation, offering a flexible and accurate solution for language-driven 3D scene understanding. Project page: https://csiro-robotics.github.io/Ilov3Splat.

Related papers

• Open-Vocabulary and Referring Segmentation for 3D Gaussians Using 2D Detectors [4.720025219010595]
  GaussDet is a method that circumvents the need for dense CLIP features by leveraging discrete, open-vocabulary 2D object detectors with referring expression capabilities.<n>Our approach enables a straightforward, zero-shot extension from simple language queries to complex referential grounding.
  arXiv  Detail & Related papers  (2026-06-29T17:58:41Z)
• ReferSplat: Referring Segmentation in 3D Gaussian Splatting [60.73702075842278]
  Referring 3D Gaussian Splatting (R3DGS)<n>Task aims to segment target objects in a 3D Gaussian scene based on natural language descriptions.<n>To address these challenges, we propose ReferSplat, a framework that explicitly models 3D Gaussian points with natural language expressions.
  arXiv  Detail & Related papers  (2025-08-11T17:59:30Z)
• GaussianVLM: Scene-centric 3D Vision-Language Models using Language-aligned Gaussian Splats for Embodied Reasoning and Beyond [56.677984098204696]
  multimodal language models are driving the development of 3D Vision-Language Models (VLMs)<n>We propose a scene-centric 3D VLM for 3D Gaussian splat scenes that employs language- and task-aware scene representations.<n>We present the first Gaussian splatting-based VLM, leveraging photorealistic 3D representations derived from standard RGB images.
  arXiv  Detail & Related papers  (2025-07-01T15:52:59Z)
• OpenSplat3D: Open-Vocabulary 3D Instance Segmentation using Gaussian Splatting [52.40697058096931]
  3D Gaussian Splatting (3DGS) has emerged as a powerful representation for neural scene reconstruction.<n>We introduce an approach for open-vocabulary 3D instance segmentation without requiring manual labeling, termed OpenSplat3D.<n>We show results on LERF-mask and LERF-OVS as well as the full ScanNet++ validation set, demonstrating the effectiveness of our approach.
  arXiv  Detail & Related papers  (2025-06-09T12:37:15Z)
• CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting [18.581169318975046]
  3D Gaussian Splatting (3DGS) offers a powerful representation for scene reconstruction, but cross-view granularity inconsistency is a problem.<n>We propose Context-Aware Gaussian Splatting (CAGS), a novel framework that addresses this challenge by incorporating spatial context into 3DGS.<n>CAGS significantly improves 3D instance segmentation and reduces fragmentation errors on datasets like LERF-OVS and ScanNet.
  arXiv  Detail & Related papers  (2025-04-16T09:20:03Z)
• OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding [20.578106363482018]
  OpenGS-SLAM is an innovative framework that utilizes 3D Gaussian representation to perform dense semantic SLAM in open-set environments.<n>Our system integrates explicit semantic labels derived from 2D models into the 3D Gaussian framework, facilitating robust 3D object-level understanding.<n>Our method achieves 10 times faster semantic rendering and 2 times lower storage costs compared to existing methods.
  arXiv  Detail & Related papers  (2025-03-03T15:23:21Z)
• Occam's LGS: An Efficient Approach for Language Gaussian Splatting [57.00354758206751]
  We show that the complicated pipelines for language 3D Gaussian Splatting are simply unnecessary.<n>We apply Occam's razor to the task at hand, leading to a highly efficient weighted multi-view feature aggregation technique.
  arXiv  Detail & Related papers  (2024-12-02T18:50:37Z)
• Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding [59.51535163599723]
  FreeGS is an unsupervised semantic-embedded 3DGS framework that achieves view-consistent 3D scene understanding without the need for 2D labels.<n>FreeGS performs comparably to state-of-the-art methods while avoiding the complex data preprocessing workload.
  arXiv  Detail & Related papers  (2024-11-29T08:52:32Z)
• OpenGaussian: Towards Point-Level 3D Gaussian-based Open Vocabulary Understanding [54.981605111365056]
  This paper introduces OpenGaussian, a method based on 3D Gaussian Splatting (3DGS) capable of 3D point-level open vocabulary understanding.<n>Our primary motivation stems from observing that existing 3DGS-based open vocabulary methods mainly focus on 2D pixel-level parsing.
  arXiv  Detail & Related papers  (2024-06-04T07:42:33Z)
• Point2Seq: Detecting 3D Objects as Sequences [58.63662049729309]
  We present a simple and effective framework, named Point2Seq, for 3D object detection from point clouds. We view each 3D object as a sequence of words and reformulate the 3D object detection task as decoding words from 3D scenes in an auto-regressive manner.
  arXiv  Detail & Related papers  (2022-03-25T00:20:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.