# Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network

arXiv: https://arxiv.org/abs/1901.08906 · HTML: https://mlsanity.com/paper/1901.08906/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2019.

## Built on by (most cited first, 30 of 33)

- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021)
- [AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers](https://mlsanity.com/paper/2301.04545/) (2023)
- [Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis](https://mlsanity.com/paper/2203.03311/) (2022)
- [No-Reference Point Cloud Quality Assessment via Domain Adaptation](https://mlsanity.com/paper/2112.02851/) (2021)
- [Bayesian Diffusion Models for 3D Shape Reconstruction](https://mlsanity.com/paper/2403.06973/) (2024)
- [Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning](https://mlsanity.com/paper/2202.12588/) (2022)
- [Patch-Based Deep Autoencoder for Point Cloud Geometry Compression](https://mlsanity.com/paper/2110.09109/) (2021)
- [HyperPocket: Generative Point Cloud Completion](https://mlsanity.com/paper/2102.05973/) (2021)
- [CCD-3DR: Consistent Conditioning in Diffusion for Single-Image 3D Reconstruction](https://mlsanity.com/paper/2308.07837/) (2023)
- [Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction](https://mlsanity.com/paper/2102.13391/) (2021)
- [T-CorresNet: Template Guided 3D Point Cloud Completion with Correspondence Pooling Query Generation Strategy](https://mlsanity.com/paper/2407.05008/) (2024)
- [Is Single-View Mesh Reconstruction Ready for Robotics?](https://mlsanity.com/paper/2505.17966/) (2025)
- [Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction](https://mlsanity.com/paper/2112.12484/) (2021)
- [Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model](https://mlsanity.com/paper/2308.08754/) (2023)
- [DMF-Net: Image-Guided Point Cloud Completion with Dual-Channel Modality Fusion and Shape-Aware Upsampling Transformer](https://mlsanity.com/paper/2406.17319/) (2024)
- [MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing](https://mlsanity.com/paper/2411.16773/) (2024)
- [Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport](https://mlsanity.com/paper/2107.14498/) (2021)
- [AE-NeRF: Auto-Encoding Neural Radiance Fields for 3D-Aware Object Manipulation](https://mlsanity.com/paper/2204.13426/) (2022)
- [Visual Enhanced 3D Point Cloud Reconstruction from A Single Image](https://mlsanity.com/paper/2108.07685/) (2021)
- [Point Cloud Completion Guided by Prior Knowledge via Causal Inference](https://mlsanity.com/paper/2305.17770/) (2023)
- [From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training](https://mlsanity.com/paper/2501.13387/) (2025)
- [ConcreTizer: Model Inversion Attack via Occupancy Classification and Dispersion Control for 3D Point Cloud Restoration](https://mlsanity.com/paper/2503.06986/) (2025)
- [QuickGrasp: Lightweight Antipodal Grasp Planning with Point Clouds](https://mlsanity.com/paper/2504.19716/) (2025)
- [SE-MD: A Single-encoder multiple-decoder deep network for point cloud generation from 2D images](https://mlsanity.com/paper/2106.15325/) (2021)
- [Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images](https://mlsanity.com/paper/2109.11844/) (2021)
- [Convolutional Neural Network-based Efficient Dense Point Cloud Generation using Unsigned Distance Fields](https://mlsanity.com/paper/2203.11537/) (2022)
- [CompleteDT: Point Cloud Completion with Dense Augment Inference Transformers](https://mlsanity.com/paper/2205.14999/) (2022)
- [3D Colored Shape Reconstruction from a Single RGB Image through Diffusion](https://mlsanity.com/paper/2302.05573/) (2023)
- [Bidirectional Knowledge Reconfiguration for Lightweight Point Cloud Analysis](https://mlsanity.com/paper/2310.05125/) (2023)

## Abstract (arXiv)

Reconstructing a high-resolution 3D model of an object is a challenging task in computer vision. Designing scalable and light-weight architectures is crucial while addressing this problem. Existing point-cloud based reconstruction approaches directly predict the entire point cloud in a single stage. Although this technique can handle low-resolution point clouds, it is not a viable solution for generating dense, high-resolution outputs. In this work, we introduce DensePCR, a deep pyramidal network for point cloud reconstruction that hierarchically predicts point clouds of increasing resolution. Towards this end, we propose an architecture that first predicts a low-resolution point cloud, and then hierarchically increases the resolution by aggregating local and global point features to deform a grid. Our method generates point clouds that are accurate, uniform and dense. Through extensive quantitative and qualitative evaluation on synthetic and real datasets, we demonstrate that DensePCR outperforms the existing state-of-the-art point cloud reconstruction works, while also providing a light-weight and scalable architecture for predicting high-resolution outputs.