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Habitat-Matterport 3D Semantics Dataset

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arxiv 2210.05633 v3 pith:4WCEDXTV submitted 2022-10-11 cs.CV

Karmesh Yadav , Ram Ramrakhya , Santhosh Kumar Ramakrishnan , Theo Gervet , John Turner , Aaron Gokaslan , Noah Maestre , Angel Xuan Chang
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Dhruv Batra Manolis Savva Alexander William Clegg Devendra Singh Chaplot
This is my paper
classification cs.CV
keywords hm3dsemdatasetobjectdatasetssemanticsspacesthoseannotations
verification ladder T0 review T1 audit T2 compute T3 formal
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We present the Habitat-Matterport 3D Semantics (HM3DSEM) dataset. HM3DSEM is the largest dataset of 3D real-world spaces with densely annotated semantics that is currently available to the academic community. It consists of 142,646 object instance annotations across 216 3D spaces and 3,100 rooms within those spaces. The scale, quality, and diversity of object annotations far exceed those of prior datasets. A key difference setting apart HM3DSEM from other datasets is the use of texture information to annotate pixel-accurate object boundaries. We demonstrate the effectiveness of HM3DSEM dataset for the Object Goal Navigation task using different methods. Policies trained using HM3DSEM perform outperform those trained on prior datasets. Introduction of HM3DSEM in the Habitat ObjectNav Challenge lead to an increase in participation from 400 submissions in 2021 to 1022 submissions in 2022.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Room-Mediated Co-occurrence for Zero-Shot Object-Centric Semantic Navigation via Frontier Scoring

    cs.RO 2026-07 conditional novelty 6.0 of 10

    An object-centric, training-free pipeline using CLIP-derived room-probability vectors to score frontiers improves zero-shot ObjectNav success by a relative 3% over an image-based baseline on HM3D.

  2. SplatSearch: Instance Image Goal Navigation for Mobile Robots using 3D Gaussian Splatting and Diffusion Models

    cs.RO 2025-11 conditional novelty 6.0 of 10

    SplatSearch combines sparse-view 3D Gaussian Splatting, multi-view diffusion inpainting, and semantic/visual frontier scoring to achieve viewpoint-invariant instance image-goal navigation in unknown environments.

  3. FUS3DMaps: Scalable and Accurate Open-Vocabulary Semantic Mapping by 3D Fusion of Voxel- and Instance-Level Layers

    cs.RO 2026-05 unverdicted novelty 5.0 of 10

    FUS3DMaps fuses voxel- and instance-level open-vocabulary layers inside a shared 3D voxel map to improve both layers and enable scalable accurate semantic mapping.

  4. Open Scene Graphs for Open-World Object-Goal Navigation

    cs.RO 2025-08 unverdicted novelty 5.0 of 10

    OSG Navigator adds auto-generated scene-graph schemas as spatial memory to foundation models, reporting SOTA ObjectNav performance with zero-shot generalization across environments, goals, and robots.

  5. SEMNAV: Enhancing Visual Semantic Navigation in Robotics through Semantic Segmentation

    cs.RO 2025-06 unverdicted novelty 5.0 of 10

    SEMNAV trains visual semantic navigation policies on semantic segmentation inputs rather than RGB, reports higher success rates in Habitat 2.0 on HM3D, and shows improved real-world transfer on robotic platforms.

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