Research map / Point clouds / Shape and surface completion
IPoD: Implicit Field Learning with Point Diffusion for Generalizable 3D Object Reconstruction from Single RGB-D Images
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Proposes IPoD, which unifies implicit field (UDF) learning with point diffusion by treating query points as a noisy point cloud denoised iteratively, plus a self-conditioning mechanism using predicted UDF values, for generalizable single RGB-D object reconstruction.
Problem
Generalizable 3D object reconstruction from single-view RGB-D images with imperfect real-world supervision; existing implicit-field Transformer methods need dense uniformly sampled query supervision.
Approach
Query points for implicit field learning double as a noisy point cloud for diffusion denoising, so queries adapt toward the object surface; the predicted UDF values are fed back as a self-condition for noise prediction.
Key results
- Transformer-based IPoD (Ours2) reaches F1 87.2 and CD 0.190 on CO3D-v2 vs NU-MCC F1 80.9 and CD 0.266 (28.6% CD reduction).
- PVCNN-based Ours1 improves PC2-depth F1 from 70.7 to 76.2 and CD from 0.312 to 0.252.
- Ablation: adding diffusion to NU-MCC raises F1 from 80.9 to 85.8; self-conditioning on UDF adds a further 1.4 points.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add diffusion learning to NU-MCC (adaptive denoised query points) (ablation) | NU-MCC (Transformer) · CO3D-v2 | F1: +4.9 (80.9→85.8) | Helped |
| add self-conditioning on predicted UDF values (ablation) | NU-MCC + diffusion (Transformer) · CO3D-v2 | F1: +1.4 (85.8→87.2) | Helped |
| add UDF implicit supervision on noisy query points to PC2-depth (ablation) | PC2-depth (PVCNN) · CO3D-v2 | F1: +2.6 (70.7→73.3) | Helped |
| add self-conditioning on predicted UDF values (ablation) | PC2-depth + UDF (PVCNN) · CO3D-v2 | F1: +2.9 (73.3→76.2) | Helped |
| use UDF+RGB as self-condition instead of UDF alone (ablation) | IPoD self-conditioning (PVCNN) · CO3D-v2 | F1: 0.0 (76.2→76.2) | No clear effect |
| use occupancy value as self-condition instead of UDF (ablation) | IPoD self-conditioning (PVCNN) · CO3D-v2 | F1: -1.7 (76.2→74.5) | Hurt |
| use classical x0-prediction as self-condition instead of predicted UDF (ablation) | IPoD self-conditioning (PVCNN) · CO3D-v2 | F1: -3.5 (76.2→72.7) | Hurt |
| switch off self-conditioning in only the 500-250 denoising stage (ablation) | IPoD (PVCNN) · CO3D-v2 | F1: -7.9 (76.2→68.1 when off at 250-0; 76.2→72.3 at 500-250) | Hurt |
| add diffusion + implicit UDF + self-conditioning over PC2-depth baseline | PVCNN backbone · CO3D-v2 | Chamfer distance: 0.312→0.252 (19.2% reduction) | Helped |
| full IPoD with Transformer backbone vs NU-MCC | Transformer backbone · CO3D-v2 | Chamfer distance: 0.266→0.190 (28.6% reduction) | Helped |
| full IPoD with Transformer backbone vs NU-MCC | Transformer backbone · CO3D-v2 | F1: 80.9→87.2 (+7.8) | Helped |
Surprising findings
- Using the predicted UDF values as self-condition outperforms the classical x0-prediction self-conditioning (76.2 vs 72.7 F1 for PVCNN variant).
Limitations
- Not validated on 3D human or scene reconstruction.
- Over-predicts extra noisy points on MVImgNet cases; no quantitative MVImgNet evaluation because GT is incomplete.
- Single-view inputs with heavy occlusion are hard for scene reconstruction.
Experimental setup
Datasets: CO3D-v2 MVImgNet
Benchmarks: CO3D-v2 MVImgNet
Metrics: Chamfer distance Acc Comp Precision Recall F-score
Baselines: PC2 PC2-depth MCC NU-MCC
Builds on: MCC NU-MCC PC2 PVD self-conditioning
Closest papers by meaning
- Coherent 3D Scene Diffusion From a Single RGB Image (NeurIPS 2024 · similarity 0.88)
- SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single Images (CVPR 2025 · similarity 0.86)
- Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Tracking (CVPR 2024 · similarity 0.86)
- Adaptive 3D Reconstruction via Diffusion Priors and Forward Curvature-Matching Likelihood Updates (NeurIPS 2025 · similarity 0.86)
- Sparse3D: Distilling Multiview-Consistent Diffusion for Object Reconstruction from Sparse Views (AAAI 2024 · similarity 0.86)
- Denoising Diffusion via Image-Based Rendering (ICLR 2024 · similarity 0.85)
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