# A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling

arXiv: https://arxiv.org/abs/2312.02719 · HTML: https://mlsanity.com/paper/2312.02719/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 38 of its 52 references that are in the map.
- First posted on arXiv 3 Dec 2023.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 38)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://mlsanity.com/paper/2103.01458/) (2021)
- [3D Shape Generation and Completion through Point-Voxel Diffusion](https://mlsanity.com/paper/2104.03670/) (2021)
- [PointCLIP: Point Cloud Understanding by CLIP](https://mlsanity.com/paper/2112.02413/) (2021)
- [Point-Voxel CNN for Efficient 3D Deep Learning](https://mlsanity.com/paper/1907.03739/) (2019)
- [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://mlsanity.com/paper/2011.13005/) (2020)
- [PU-Net: Point Cloud Upsampling Network](https://mlsanity.com/paper/1801.06761/) (2018)
- [PU-GAN: a Point Cloud Upsampling Adversarial Network](https://mlsanity.com/paper/1907.10844/) (2019)
- [Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models](https://mlsanity.com/paper/2212.14704/) (2022)
- [Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction](https://mlsanity.com/paper/1706.07036/) (2017)
- [Variational Relational Point Completion Network](https://mlsanity.com/paper/2104.10154/) (2021)
- [Patch-based Progressive 3D Point Set Upsampling](https://mlsanity.com/paper/1811.11286/) (2018)
- [PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks](https://mlsanity.com/paper/1912.03264/) (2019)
- [A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion](https://mlsanity.com/paper/2112.03530/) (2021)

## Built on by (most cited first, 28 of 28)

- [An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models](https://mlsanity.com/paper/2411.16308/) (2024)
- [SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization](https://mlsanity.com/paper/2503.14558/) (2025)
- [ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation](https://mlsanity.com/paper/2408.02226/) (2024)
- [WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusion](https://mlsanity.com/paper/2504.13561/) (2025)
- [Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion](https://mlsanity.com/paper/2508.03252/) (2025)
- [PointNSP: Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction](https://mlsanity.com/paper/2510.05613/) (2025)
- [PCA-Seg: Revisiting Cost Aggregation for Open-Vocabulary Semantic and Part Segmentation](https://mlsanity.com/paper/2603.17520/) (2026)
- [Deep Learning for 3D Point Cloud Enhancement: A Survey](https://mlsanity.com/paper/2411.00857/) (2024)
- [A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation](https://mlsanity.com/paper/2511.19004/) (2025)
- [MBPU: A Plug-and-Play State Space Model for Point Cloud Upsamping with Fast Point Rendering](https://mlsanity.com/paper/2410.15941/) (2024)
- [CoopDiff: A Diffusion-Guided Approach for Cooperation under Corruptions](https://mlsanity.com/paper/2603.01688/) (2026)
- [AR2-4FV: Anchored Referring and Re-identification for Long-Term Grounding in Fixed-View Videos](https://mlsanity.com/paper/2603.07758/) (2026)
- [Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosis](https://mlsanity.com/paper/2603.12997/) (2026)
- [CNMBI: Determining the Number of Clusters Using Center Pairwise Matching and Boundary Filtering](https://mlsanity.com/paper/2603.26744/) (2026)
- [PointNSP: Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction](https://mlsanity.com/paper/2503.08594/) (2025)
- [PointGAC: Geometric-Aware Codebook for Masked Point Cloud Modeling](https://mlsanity.com/paper/2507.04801/) (2025)
- [Preserving Topological and Geometric Embeddings for Point Cloud Recovery](https://mlsanity.com/paper/2507.19121/) (2025)
- [PVNet: Point-Voxel Interaction LiDAR Scene Upsampling Via Diffusion Models](https://mlsanity.com/paper/2508.17050/) (2025)
- [IPCD: Intrinsic Point-Cloud Decomposition](https://mlsanity.com/paper/2511.09866/) (2025)
- [LAHNet: Local Attentive Hashing Network for Point Cloud Registration](https://mlsanity.com/paper/2512.00927/) (2025)
- [PUFM++: Point Cloud Upsampling via Enhanced Flow Matching](https://mlsanity.com/paper/2512.20988/) (2025)
- [Beyond Geometry: Artistic Disparity Synthesis for Immersive 2D-to-3D](https://mlsanity.com/paper/2603.05906/) (2026)
- [Learnable Instance Attention Filtering for Adaptive Detector Distillation](https://mlsanity.com/paper/2603.26088/) (2026)
- [Diff-KD: Diffusion-based Knowledge Distillation for Collaborative Perception under Corruptions](https://mlsanity.com/paper/2604.02061/) (2026)
- [The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery](https://mlsanity.com/paper/2604.14176/) (2026)
- [Super-Resolution of Airborne Laser Scanning Point Clouds for Forest Inventory](https://mlsanity.com/paper/2605.02201/) (2026)
- [B'ezier Degradation Modeling for LiDAR-based Human Motion Capture](https://mlsanity.com/paper/2605.19620/) (2026)
- [T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation](https://mlsanity.com/paper/2606.30147/) (2026)

## Abstract (arXiv)

Point cloud upsampling (PCU) enriches the representation of raw point clouds, significantly improving the performance in downstream tasks such as classification and reconstruction. Most of the existing point cloud upsampling methods focus on sparse point cloud feature extraction and upsampling module design. In a different way, we dive deeper into directly modelling the gradient of data distribution from dense point clouds. In this paper, we proposed a conditional denoising diffusion probability model (DDPM) for point cloud upsampling, called PUDM. Specifically, PUDM treats the sparse point cloud as a condition, and iteratively learns the transformation relationship between the dense point cloud and the noise. Simultaneously, PUDM aligns with a dual mapping paradigm to further improve the discernment of point features. In this context, PUDM enables learning complex geometry details in the ground truth through the dominant features, while avoiding an additional upsampling module design. Furthermore, to generate high-quality arbitrary-scale point clouds during inference, PUDM exploits the prior knowledge of the scale between sparse point clouds and dense point clouds during training by parameterizing a rate factor. Moreover, PUDM exhibits strong noise robustness in experimental results. In the quantitative and qualitative evaluations on PU1K and PUGAN, PUDM significantly outperformed existing methods in terms of Chamfer Distance (CD) and Hausdorff Distance (HD), achieving state of the art (SOTA) performance.