Papers › DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation

DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation

24 Mar 2025arXiv:2503.18944archive 2025-07-28

Karim Abou Zeid, Kadir Yilmaz, Daan de Geus, Alexander Hermans, David Adrian, Timm Linder, Bastian Leibe

Vision foundation models (VFMs) trained on large-scale image datasets provide high-quality features that have significantly advanced 2D visual recognition. However, their potential in 3D vision remains largely untapped, despite the common availability of 2D images alongside 3D point cloud datasets. While significant research has been dedicated to 2D-3D fusion, recent state-of-the-art 3D methods predominantly focus on 3D data, leaving the integration of VFMs into 3D models underexplored. In this work, we challenge this trend by introducing DITR, a simple yet effective approach that extracts 2D foundation model features, projects them to 3D, and finally injects them into a 3D point cloud segmentation model. DITR achieves state-of-the-art results on both indoor and outdoor 3D semantic segmentation benchmarks. To enable the use of VFMs even when images are unavailable during inference, we further propose to distill 2D foundation models into a 3D backbone as a pretraining task. By initializing the 3D backbone with knowledge distilled from 2D VFMs, we create a strong basis for downstream 3D segmentation tasks, ultimately boosting performance across various datasets.

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VisualComputingInstitute/DITR officialmentioned on GitHubAGPL-3.0 report

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Tasks

3D Semantic SegmentationLIDAR Semantic SegmentationPoint Cloud SegmentationSegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Semantic Segmentation ScanNet++ DITR Top-1 IoU 0.525 #1 of 8 Archive leaderboard report
3D Semantic Segmentation ScanNet++ DITR Top-3 IoU 0.762 #1 of 8 Archive leaderboard report
3D Semantic Segmentation ScanNet200 DITR test mIoU 44.9 #1 of 16 Archive leaderboard report
3D Semantic Segmentation ScanNet200 DITR val mIoU 41.2 #1 of 16 Archive leaderboard report
3D Semantic Segmentation SemanticKITTI DITR test mIoU 74.4% #5 of 45 Archive leaderboard report
3D Semantic Segmentation SemanticKITTI DITR val mIoU 69.0% #5 of 45 Archive leaderboard report
3D Semantic Segmentation Waymo Open Dataset DITR mIoU 73.3 #1 of 2 Archive leaderboard report
LIDAR Semantic Segmentation nuScenes DITR test mIoU 0.851 #1 of 36 Archive leaderboard report
LIDAR Semantic Segmentation nuScenes DITR val mIoU 0.842 #1 of 36 Archive leaderboard report
Semantic Segmentation S3DIS Area5 D-DITR mIoU 75.0 #3 of 61 Archive leaderboard report
Semantic Segmentation S3DIS Area5 DITR mIoU 74.1 #7 of 61 Archive leaderboard report
Semantic Segmentation ScanNet DITR test mIoU 79.7 #1 of 45 Archive leaderboard report
Semantic Segmentation ScanNet DITR val mIoU 80.5 #1 of 45 Archive leaderboard report

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