# Depth-Semantic Alignment and Affinity-Guided Fusion for Structured Radar Point Cloud Generation

arXiv: https://arxiv.org/abs/2606.26743 · HTML: https://mlsanity.com/paper/2606.26743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image](https://mlsanity.com/paper/1709.07492/) (2017)
- [CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2011.04841/) (2020)
- [Learning Affinity via Spatial Propagation Networks](https://mlsanity.com/paper/1710.01020/) (2017)
- [A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection](https://mlsanity.com/paper/2005.07431/) (2020)
- [Monocular Depth Estimation Based On Deep Learning: An Overview](https://mlsanity.com/paper/2003.06620/) (2020)
- [4D Millimeter-Wave Radar in Autonomous Driving: A Survey](https://mlsanity.com/paper/2306.04242/) (2023)
- [A Survey on Deep Stereo Matching in the Twenties](https://mlsanity.com/paper/2407.07816/) (2024)
- [MiliPoint: A Point Cloud Dataset for mmWave Radar](https://mlsanity.com/paper/2309.13425/) (2023)
- [DREAM-PCD: Deep Reconstruction and Enhancement of mmWave Radar Pointcloud](https://mlsanity.com/paper/2309.15374/) (2023)
- [Scalable Autoregressive Monocular Depth Estimation](https://mlsanity.com/paper/2411.11361/) (2024)
- [A Real-Time Error Prevention System for Gaze-Based Interaction in Virtual Reality Based on Anomaly Detection](https://mlsanity.com/paper/2601.15146/) (2026)
- [Enhancing mmWave Radar Point Cloud via Visual-inertial Supervision](https://mlsanity.com/paper/2404.17229/) (2024)
- [Diffusion-Based mmWave Radar Point Cloud Enhancement Driven by Range Images](https://mlsanity.com/paper/2503.02300/) (2025)

## Abstract (arXiv)

Point clouds are an important carrier of three-dimensional spatial information, and their quality directly affects the performance of downstream perception tasks such as object detection and tracking. However, millimeter-wave radar point clouds are typically sparse, noisy, and structurally incomplete. To address these limitations, this paper proposes a multimodal point cloud generation method based on vision-radar fusion. The proposed method leverages image semantic information to impose structural constraints and achieve spatial alignment for radar point clouds, while incorporating a sparse completion strategy to enhance point density and recover missing structures. The generated point clouds are further evaluated in object detection and tracking tasks. Experimental results demonstrate that the proposed method effectively improves point cloud quality and enhances the detection accuracy and robustness of perception models in complex environments, providing a practical solution for multisensor point cloud generation and intelligent perception systems.