# FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing

arXiv: https://arxiv.org/abs/2511.07665 · HTML: https://mlsanity.com/paper/2511.07665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 56 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 27 of 27)

- [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)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies](https://mlsanity.com/paper/2206.04670/) (2022)
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review](https://mlsanity.com/paper/2004.05224/) (2020)
- [OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization](https://mlsanity.com/paper/2304.07493/) (2023)
- [Object DGCNN: 3D Object Detection using Dynamic Graphs](https://mlsanity.com/paper/2110.06923/) (2021)
- [Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstruction](https://mlsanity.com/paper/2304.12467/) (2023)
- [Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity](https://mlsanity.com/paper/2008.13006/) (2020)
- [YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud](https://mlsanity.com/paper/1808.02350/) (2018)
- [PointAcc: Efficient Point Cloud Accelerator](https://mlsanity.com/paper/2110.07600/) (2021)
- [PointVector: A Vector Representation In Point Cloud Analysis](https://mlsanity.com/paper/2205.10528/) (2022)
- [PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learning](https://mlsanity.com/paper/2405.15214/) (2024)
- [Mesorasi: Architecture Support for Point Cloud Analytics via Delayed-Aggregation](https://mlsanity.com/paper/2008.06967/) (2020)
- [A 3D-Deep-Learning-based Augmented Reality Calibration Method for Robotic Environments using Depth Sensor Data](https://mlsanity.com/paper/1912.12101/) (2019)
- [Crescent: Taming Memory Irregularities for Accelerating Deep Point Cloud Analytics](https://mlsanity.com/paper/2204.10707/) (2022)
- [Accelerating Sparse DNNs Based on Tiled GEMM](https://mlsanity.com/paper/2402.10876/) (2024)
- [Visualization and Labeling of Point Clouds in Virtual Reality](https://mlsanity.com/paper/1804.04111/) (2018)
- [Tigris: Architecture and Algorithms for 3D Perception in Point Clouds](https://mlsanity.com/paper/1911.07841/) (2019)
- [SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations](https://mlsanity.com/paper/2309.10527/) (2023)
- [Towards Accurate and Efficient 3D Object Detection for Autonomous Driving: A Mixture of Experts Computing System on Edge](https://mlsanity.com/paper/2507.04123/) (2025)
- [PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud](https://mlsanity.com/paper/2211.15759/) (2022)
- [Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Survey](https://mlsanity.com/paper/2402.12923/) (2024)
- [HgPCN: A Heterogeneous Architecture for E2E Embedded Point Cloud Inference](https://mlsanity.com/paper/2501.07767/) (2025)

## Abstract (arXiv)

Three-dimensional (3D) point clouds are increasingly used in applications such as autonomous driving, robotics, and virtual reality (VR). Point-based neural networks (PNNs) have demonstrated strong performance in point cloud analysis, originally targeting small-scale inputs. However, as PNNs evolve to process large-scale point clouds with hundreds of thousands of points, all-to-all computation and global memory access in point cloud processing introduce substantial overhead, causing O(n2) computational complexity and memory traffic where n is the number of points. Existing accelerators, primarily optimized for small-scale workloads, overlook this challenge and scale poorly due to inefficient partitioning and non-parallel architectures. To address these issues, we propose FractalCloud, a fractal-inspired hardware architecture for efficient large-scale 3D point cloud processing. FractalCloud introduces two key optimizations: (1) a co-designed Fractal method for shape-aware and hardware-friendly partitioning, and (2) block-parallel point operations that decompose and parallelize all point operations. A dedicated hardware design with on-chip fractal and flexible parallelism further enables fully parallel processing within limited memory resources. Implemented in 28 nm technology as a chip layout with a core area of 1.5 mm2, FractalCloud achieves 21.7x speedup and 27x energy reduction over state-of-the-art accelerators while maintaining network accuracy, demonstrating its scalability and efficiency for PNN inference.