# Inferring Point Clouds from Single Monocular Images by Depth Intermediation

arXiv: https://arxiv.org/abs/1812.01402 · HTML: https://mlsanity.com/paper/1812.01402/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [Can Language Understand Depth?](https://mlsanity.com/paper/2207.01077/) (2022)
- [SPARC: Sparse Render-and-Compare for CAD model alignment in a single RGB image](https://mlsanity.com/paper/2210.01044/) (2022)
- [Leveraging Geometry for Shape Estimation from a Single RGB Image](https://mlsanity.com/paper/2111.05615/) (2021)
- [Sparse Multi-Object Render-and-Compare](https://mlsanity.com/paper/2310.11184/) (2023)
- [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)
- [Lightweight Prompt-Guided CLIP Adaptation for Monocular Depth Estimation](https://mlsanity.com/paper/2604.01118/) (2026)

## Abstract (arXiv)

In this paper, we propose a pipeline to generate 3D point cloud of an object from a single-view RGB image. Most previous work predict the 3D point coordinates from single RGB images directly. We decompose this problem into depth estimation from single images and point cloud completion from partial point clouds. Our method sequentially predicts the depth maps from images and then infers the complete 3D object point clouds based on the predicted partial point clouds. We explicitly impose the camera model geometrical constraint in our pipeline and enforce the alignment of the generated point clouds and estimated depth maps. Experimental results for the single image 3D object reconstruction task show that the proposed method outperforms existing state-of-the-art methods. Both the qualitative and quantitative results demonstrate the generality and suitability of our method.