mlsanityData to 7 Oct 2026

CDSeg: A Renderable Gaussian Carrier for Image-to-3D Label Transfer

arXiv 2608.05482 ↗First posted 6 Aug 2026cs.CVcited by 0

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Abstract (arXiv)

Modern image models provide strong cues about what should be segmented in each view, but their masks do not by themselves determine where those labels should persist in 3D. We present Cross-Domain Segmentation via Gaussian Splatting (CDSeg), a label-transfer interface that requires no task-specific 3D segmentation training and uses Gaussian primitives as a renderable label carrier. An external mask source supplies the labels, while renderer-derived visibility determines which 3D primitives receive them. The carrier is instantiated either by completing each input point into one Gaussian, preserving its index, or by reusing the native primitives of an optimized Gaussian scene. CDSeg records pixel--primitive associations during rendering and fuses multi-view masks through voting and a local filter. The resulting labels can be returned to the original points, retained on the native Gaussian scene, or rendered into other views. CDSeg covers promptable, automatic instance, semantic, and LiDAR settings and processes scenes with millions of primitives in seconds. It obtains 92.35% mIoU on DesktopObjects-360, 95.89% on NeRDS-360, and 65.77% on the full ScanNet-v2 validation split using the provided 2D semantic annotations. CDSeg thereby provides one interface for reusing 2D masks across point clouds, Gaussian scenes, and image views without a task-specific 3D segmentation network.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 31 references that are in the map. First posted on arXiv 6 Aug 2026.

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30 of 31 references.

References · 30

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023Segment Anythingcited by 8,158
  3. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  4. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  6. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  7. 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
  8. 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,061
  9. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  10. 2023Point Transformer V3: Simpler, Faster, Strongercited by 579
  11. 2021KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3Dcited by 570
  12. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
18 more
  1. 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 502
  2. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 432
  3. 2023LangSplat: 3D Language Gaussian Splattingcited by 410
  4. 2023Gaussian Grouping: Segment and Edit Anything in 3D Scenescited by 387
  5. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 384
  6. 2023Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fieldscited by 361
  7. 2023Language Embedded 3D Gaussians for Open-Vocabulary Scene Understandingcited by 205
  8. 2017ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scanscited by 103
  9. 2021One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentationcited by 89
  10. 2024FlashSplat: 2D to 3D Gaussian Splatting Segmentation Solved Optimallycited by 57
  11. 2022Contrastive Boundary Learning for Point Cloud Segmentationcited by 56
  12. 2023NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenescited by 53
  13. 2022Meta Architecture for Point Cloud Analysiscited by 39
  14. 2024LUDVIG: Learning-Free Uplifting of 2D Visual Features to Gaussian Splatting Scenescited by 36
  15. 2023PointDC:Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clusteringcited by 15
  16. 2022PCSCNet: Fast 3D Semantic Segmentation of LiDAR Point Cloud for Autonomous Car using Point Convolution and Sparse Convolution Networkcited by 14
  17. 20243D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidancecited by 12
  18. 2026PointGS: Semantic-Consistent Unsupervised 3D Point Cloud Segmentation with 3D Gaussian Splattingcited by 1

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