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Point Cloud Pre-training with Diffusion Models
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Proposes PointDif, a diffusion-based point cloud pre-training framework where a conditional point generator (CANet + CPDM) denoises noisy point clouds conditioned on encoder features, plus a recurrent uniform timestep sampling strategy.
Problem
Point cloud pre-training is hard due to unordered, non-uniform point density; existing contrastive and masked-reconstruction methods fail to capture global density distribution and comprehensive geometric priors.
Approach
Treat point cloud pre-training as conditional point-to-point denoising: a transformer encoder's features are aggregated by CANet into a condition that guides a point diffusion model (PCNet-based, reset-gate conditioning) to recover the clean cloud from noisy versions; timesteps are sampled uniformly across recurrent intervals during training so the backbone learns balanced multi-level geometric pr
Key results
- PointDif reaches 70.0% mIoU on S3DIS Area 5, versus 68.4% for Point-MAE and 68.9% for Point-BERT.
- On ScanObjectNN, PointDif reaches 91.91 / 93.29 / 87.61 overall accuracy on OBJ-ONLY / OBJ-BG / PB-T50-RS, exceeding TAP by 2.4%, 2.9%, and 1.9%.
- On ScanNetV2 detection with 3DETR, PointDif reaches 43.7 AP50, above Point-MAE (42.8) and TAP (41.4); on SemanticKITTI with MinkowskiNet it reaches 71.3 mIoU, up 1.1% over training from scratch.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Pre-train with PointDif rather than TAP | Point cloud classification · ScanObjectNN OBJ-ONLY | Overall Accuracy (%): 89.50 -> 91.91 | Helped |
| Pre-train with PointDif rather than TAP | Point cloud classification · ScanObjectNN OBJ-BG | Overall Accuracy (%): 90.36 -> 93.29 | Helped |
| Pre-train with PointDif rather than TAP | Point cloud classification · ScanObjectNN PB-T50-RS | Overall Accuracy (%): 85.67 -> 87.61 | Helped |
| Pre-train the 3DETR encoder with PointDif | 3DETR · ScanNet | AP50 (%): 37.9 -> 43.7 | Helped |
| Pre-train PointNeXt with PointDif | PointNeXt · S3DIS Area 5 | mIoU (%): 68.5 -> 70.0 | Helped |
| Pre-train MinkowskiNet with PointDif | MinkowskiNet · SemanticKITTI val | mIoU (%): 70.2 -> 71.3 | Helped |
| Pre-train CAGroup with PointDif | CAGroup · ScanNet | AP25 (%): 73.20 -> 74.14 | Helped |
| Pre-train CAGroup with PointDif | CAGroup · ScanNet | AP50 (%): 60.84 -> 61.31 | Helped |
| Replace the point condition network with pointwise concatenation (ablation) | PointDif · S3DIS Area 5 | mIoU (%): 70.02 -> 69.43 | Hurt |
| Replace the point condition network with cross-attention guidance (ablation) | PointDif · S3DIS Area 5 | mIoU (%): 70.02 -> 69.09 | Hurt |
| Add recurrent uniform sampling across 4 intervals rather than random single-step sampling (ablation) | PointDif · S3DIS Area 5 | mIoU (%): 69.51 -> 70.02 | Helped |
| Restrict pre-training time steps to [1, 500] instead of [1, 2000] (ablation) | PointDif · ScanObjectNN OBJ-ONLY | Overall Accuracy (%): 91.91 -> 92.43 | Helped |
Surprising findings
- Cross-attention guidance performed worse than simple pointwise concatenation for conditioning the point diffusion model (69.09 vs 69.43 mIoU).
- Early-interval timesteps yield better classification features while later-interval timesteps yield better segmentation features.
Limitations
- Gains over prior pre-training are modest on some tasks (e.g., +0.9 AP25 and +0.5 AP50 on CAGroup3D detection, +0.5 AP50 over MaskPoint-style baselines on ScanNet).
- Segmentation fine-tuning freezes the pre-trained encoder, so results depend on that protocol.
- Pre-training is limited to ShapeNet, a synthetic dataset.
Experimental setup
Datasets: ShapeNet ScanObjectNN ScanNetV2 S3DIS SemanticKITTI ScanNet-Medium
Benchmarks: ScanObjectNN ScanNetV2 S3DIS Area 5 SemanticKITTI
Metrics: overall accuracy mean IoU mean accuracy average precision
Baselines: Point-BERT MaskPoint Point-MAE TAP Point-M2AE PointContrast DepthContrast DiffMAE
Builds on: DDPM Point-MAE Point-BERT PointNeXt 3DETR
Closest papers by meaning
- Multi-View Representation is What You Need for Point-Cloud Pre-Training (ICLR 2024 · similarity 0.88)
- PointInfinity: Resolution-Invariant Point Diffusion Models (CVPR 2024 · similarity 0.88)
- Point Cloud Upsampling Using Conditional Diffusion Module with Adaptive Noise Suppression (CVPR 2025 · similarity 0.88)
- FreePoint: Unsupervised Point Cloud Instance Segmentation (CVPR 2024 · similarity 0.88)
- Where Precision Meets Efficiency: Transformation Diffusion Model for Point Cloud Registration (AAAI 2025 · similarity 0.88)
- Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling (AAAI 2024 · similarity 0.87)
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