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ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning
Guocheng Qian, Hasan Abed Al Kader Hammoud, Guohao Li, Ali Thabet, Bernard Ghanem
TL;DR
PointNet++ and other point-cloud methods face computational costs, while isotropic operations are identified as an accuracy constraint. The paper develops separable and anisotropic Set Abstraction modules and uses ASSA in ASSANet; across several point-cloud tasks, the authors report improved accuracy and faster inference than prior methods.
Problem
PointNet++ incurs computational cost from MLPs on neighborhood features, and isotropic neighbor operations limit accuracy.
Source: §3.1 · p. 4, §3.2 · p. 5Method
The paper separates feature processing from neighborhood aggregation, adds geometric-aware anisotropic reduction, and replaces PointNet++'s vanilla SA modules with ASSA to form ASSANet.
Source: §3.2 · pp. 5–6, §6 · p. 9Results
Takeaways & Limitations
Abstract
from arXiv · showhide
Access to 3D point cloud representations has been widely facilitated by LiDAR sensors embedded in various mobile devices. This has led to an emerging need for fast and accurate point cloud processing techniques. In this paper, we revisit and dive deeper into PointNet++, one of the most influential yet under-explored networks, and develop faster and more accurate variants of the model. We first present a novel Separable Set Abstraction (SA) module that disentangles the vanilla SA module used in PointNet++ into two separate learning stages: (1) learning channel correlation and (2) learning spatial correlation. The Separable SA module is significantly faster than the vanilla version, yet it achieves comparable performance. We then introduce a new Anisotropic Reduction function into our Separable SA module and propose an Anisotropic Separable SA (ASSA) module that substantially increases the network's accuracy. We later replace the vanilla SA modules in PointNet++ with the proposed ASSA module, and denote the modified network as ASSANet. Extensive experiments on point cloud classification, semantic segmentation, and part segmentation show that ASSANet outperforms PointNet++ and other methods, achieving much higher accuracy and faster speeds. In particular, ASSANet outperforms PointNet++ by $7.4$ mIoU on S3DIS Area 5, while maintaining $1.6 \times $ faster inference speed on a single NVIDIA 2080Ti GPU. Our scaled ASSANet variant achieves $66.8$ mIoU and outperforms KPConv, while being more than $54 \times$ faster.
1 Introduction
Point-cloud processing methods often trade computational efficiency for accuracy, motivating designs suited to hardware-limited devices. The paper revisits PointNet++ and introduces ASSANet, which the authors report is faster and more accurate than prior methods.
1 Introduction
- Point-cloud methods can be computationally expensive, making accuracy and efficiency important for mobile devices and embedded systems with limited computational power.Source: §1 · p. 1
- PointNet++ uses Set Abstraction to sample points, group neighborhoods, extract local information with MLPs, and aggregate it; the paper studies its architecture and latency to improve speed and accuracy.The authors contrast its accuracy-efficiency balance with later methods that often improve performance at the cost of speed.Source: §1 · p. 2
- Scaled ASSANet outperforms prior state-of-the-art methods with higher accuracy and faster inference, including speed gains over DeepGCN, KPConv, PosPool*(S), and PVCNN.The reported speed gains are 294× over DeepGCN, 54× over KPConv, 9× over PosPool*(S), and 2× over PVCNN.Source: §1 · p. 2, Figure 1 · p. 2
2 Related Work
Point-cloud methods include projection-based approaches and methods that operate directly on points. Related work also explores efficient architectures, though the authors argue that existing efficient methods still perform convolutions on neighborhood features.
2 Related Work
- Projection-based methods use views or voxels with CNN backbones, but constructing these representations adds computational cost and projection can lose geometric information.Source: §2 · pp. 2–3
- Point-based methods include graph, pseudo-grid, and adaptive-weight approaches; some require sophisticated tuning or expensive graph kernels.Source: §2 · p. 3
- Efficient point-cloud methods use strategies such as random sampling, voxelization, or group-wise matrix multiplication, but the authors characterize them as still convolving neighborhood features.The authors note that voxelization could further reduce ASSANet's neighborhood-query latency as future work.Source: §2 · p. 3
3 Methodology
PointNet++ builds hierarchical point representations by subsampling points and aggregating local-neighbor features, but neighborhood MLPs add unnecessary computation and isotropic neighbor processing limits representation. The paper introduces separable and anisotropic SA variants, replaces PointNet++’s SA modules with ASSA, and describes width and depth scaling.
3.1 Preliminary: PointNet++
- PointNet++ hierarchically subsamples points and uses Set Abstraction to aggregate local-neighbor features into a backbone of multilevel representations.Vanilla SA groups K neighbors, applies MLPs, then reduces across the neighborhood.Source: §3.1 · pp. 3–4
- Neighborhood MLPs unnecessarily increase computational cost, while isotropic neighbor operations treat local neighbors equally and limit representation capability.The MLP computation contributes to over 70% of PointNet++ latency across the studied input sizes.Source: §3.1 · p. 4
3.2 Anisotropic Separable Set Abstraction (ASSA)
- PreConv SA moves MLPs onto point features before grouping, while Separable SA splits MLPs before grouping and after reduction and adds a residual connection.ASSA processes MLPs directly on point features, reducing vanilla SA’s MLP FLOPs by roughly K times; K is typically around 32.Source: §3.2 · pp. 4–6
- ASSA adds a geometric-aware anisotropic reduction that weights neighborhood features by relative x, y, and z positions before reduction.The three scaled features are concatenated, a bottleneck reduces channels by a factor of 3, and a post-reduction MLP output is added to the pre-reduction output.Source: §3.2 · pp. 5–6
3.3 ASSANet
- ASSANet replaces PointNet++’s vanilla SA modules with ASSA while keeping the other architecture and sampling configurations the same.The unchanged settings include four SA modules, D = 2 aggregation blocks, and L = 3 MLP layers per block.Source: §3.3 · p. 6
3.4 Scaling ASSANet
- ASSANet can be scaled by changing channel width or the number of stacked aggregation blocks D; the paper highlights ASSANet (L) with C = 128 and D = 3.Reducing D to 1 is described as making ASSANet faster, while increasing D is described as improving accuracy.Source: §3.4 · p. 6
4 Experiments
Experiments evaluate ASSANet and ASSANet (L) on S3DIS scene segmentation, ModelNet40 classification, and ShapeNetPart segmentation. Across these benchmarks, the results report higher accuracy and faster inference than PointNet++ and competitive accuracy with faster speeds than state-of-the-art methods.
4 Experiments
- Experiments use PosPool's data-processing and evaluation protocols; S3DIS models train on Areas 1–4 and 6 and test on Area 5.Source: §4 · p. 7, §4.1 · p. 7
4.1 3D Scene Segmentation
- On S3DIS Area 5, ASSANet outperforms PointNet++ by 7 mIoU and is 1.6× faster.Source: Table 1 · p. 7, §4.1 · p. 7
- On S3DIS, ASSANet (L) is 54.6× faster than KPConv and 7.9× faster than PosPool*.Source: §4.1 · p. 7
4.2 3D Object Classification
- On ModelNet40, ASSANet exceeds PointNet++ by 1.7 units in overall accuracy and is 2.1× faster.Source: §4.2 · p. 7, Table 2 · p. 8
4.3 3D Part Segmentation
- On ShapeNetPart, ASSANet (L) reaches 86.1% mIoU, on par with state-of-the-art accuracy, and is nearly 7.8× faster than KPConv.Source: §4.3 · p. 8
5 Ablation Study
The ablation study evaluates proposed SA variants and how width and depth scaling affect accuracy and inference speed. ASSA improves on Separable SA, while scaling gains come with speed costs.
5.1 Ablation on Proposed SA variants
- All proposed SA variants are over 1.6× faster than vanilla SA; Separable SA improves PreConv accuracy by 3.7 mIoU, and ASSA further improves accuracy over Separable SA while outperforming other methods and remaining faster.The study also reports that ASSA feature patterns capture geometric relationships, such as links between a plane’s tail and wings.Source: Table 4 · p. 8, §5.1 · pp. 8–9
5.2 Ablation on Scaling Regimes
- Increasing width from C=3 to C=8 raises accuracy from 41.21 mIoU to 53.95 with negligible speed loss, but gains become marginal and speed drops noticeably beyond C ≥128.Figure 4 (left) presents the width-scaling trend.Source: §5.2 · p. 9, Figure 4 · p. 9
- At C=128, increasing depth improves accuracy when D ≤3, then rapidly saturates, while inference speed decreases linearly.Figure 4 (right) presents the depth-scaling trend.Source: §5.2 · p. 9, Figure 4 · p. 9
6 Conclusion
The paper develops PointNet++ variants to reduce computational cost and improve accuracy, culminating in ASSANet. Experiments report improved accuracy and faster speed across point-cloud tasks, with scaled ASSANet achieving new state-of-the-art results on various tasks at faster speeds.
6 Conclusion
- The authors identify neighborhood-feature MLP computation and isotropic operations as sources of PointNet++’s computational burden and accuracy limits, respectively.They propose PreConv SA, Separable SA, and ASSA, then replace PointNet++’s vanilla SA modules to form ASSANet.Source: §6 · p. 9
- Experiments across classification, semantic segmentation, and part segmentation report improved accuracy and faster speed for ASSANet, while scaled ASSANet achieves new state-of-the-art results on various tasks at faster speeds.The authors suggest random sampling and voxelization for further speed improvements, or studying compound scaling as future work.Source: §6 · p. 9