# HLS4PC: A Parametrizable Framework For Accelerating Point-Based 3D Point Cloud Models on FPGA

arXiv: https://arxiv.org/abs/2512.22139 · HTML: https://mlsanity.com/paper/2512.22139/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 12 of 12)

- [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)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data](https://mlsanity.com/paper/1908.04616/) (2019)
- [Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework](https://mlsanity.com/paper/2202.07123/) (2022)
- [PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling](https://mlsanity.com/paper/2003.00492/) (2020)
- [hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices](https://mlsanity.com/paper/2103.05579/) (2021)
- [PointHop: An Explainable Machine Learning Method for Point Cloud Classification](https://mlsanity.com/paper/1907.12766/) (2019)
- [PointAcc: Efficient Point Cloud Accelerator](https://mlsanity.com/paper/2110.07600/) (2021)
- [A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation](https://mlsanity.com/paper/2405.11903/) (2024)
- [PointNet on FPGA for Real-Time LiDAR Point Cloud Processing](https://mlsanity.com/paper/2006.00049/) (2020)
- [An Efficient FPGA Accelerator for Point Cloud](https://mlsanity.com/paper/2210.07803/) (2022)

## Built on by (most cited first, 1 of 1)

- [H3DNAS: Hardware-Aware ONNX-Native 3D Point Cloud Model Compression](https://mlsanity.com/paper/2609.02684/) (2026)

## Abstract (arXiv)

Point-based 3D point cloud models employ computation and memory intensive mapping functions alongside NN layers for classification/segmentation, and are executed on server-grade GPUs. The sparse, and unstructured nature of 3D point cloud data leads to high memory and computational demand, hindering real-time performance in safety critical applications due to GPU under-utilization. To address this challenge, we present HLS4PC, a parameterizable HLS framework for FPGA acceleration. Our approach leverages FPGA parallelization and algorithmic optimizations to enable efficient fixed-point implementations of both mapping and NN functions. We explore several hardware-aware compression techniques on a state-of-the-art PointMLP-Elite model, including replacing FPS with URS, parameter quantization, layer fusion, and input-points pruning, yielding PointMLP-Lite, a 4x less complex variant with only 2% accuracy drop on ModelNet40. Secondly, we demonstrate that the FPGA acceleration of the PointMLP-Lite results in 3.56x higher throughput than previous works. Furthermore, our implementation achieves 2.3x and 22x higher throughput compared to the GPU and CPU implementations, respectively.