Papers › 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation
3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation
Angela Dai, Matthias Nießner
We present 3DMV, a novel method for 3D semantic scene segmentation of RGB-D scans in indoor environments using a joint 3D-multi-view prediction network. In contrast to existing methods that either use geometry or RGB data as input for this task, we combine both data modalities in a joint, end-to-end network architecture. Rather than simply projecting color data into a volumetric grid and operating solely in 3D -- which would result in insufficient detail -- we first extract feature maps from associated RGB images. These features are then mapped into the volumetric feature grid of a 3D network using a differentiable backprojection layer. Since our target is 3D scanning scenarios with possibly many frames, we use a multi-view pooling approach in order to handle a varying number of RGB input views. This learned combination of RGB and geometric features with our joint 2D-3D architecture achieves significantly better results than existing baselines. For instance, our final result on the ScanNet 3D segmentation benchmark increases from 52.8\% to 75\% accuracy compared to existing volumetric architectures.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Segmentation | ScanNet | 3DMV | Average Accuracy | 75.0% | #1 of 3 | Archive leaderboard | report |
| Semantic Segmentation | ScanNet | 3DMV | test mIoU | 48.4 | #39 of 45 | Archive leaderboard | report |
| Semantic Segmentation | ScanNetV2 | 3DMV (2d proj) | Mean IoU | 49.8% | #7 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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