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DEF-desenat — Point Cloud Adversarial Defense via Shapley-Guided Desensitization
Part of the ANIMA Perception Suite by Robot Flow Labs.
Paper
Desensitizing for Improving Corruption Robustness in Point Cloud Classification through Adversarial Training Published in Pattern Recognition (Elsevier), 2025 Reference: JerkyT/DesenAT
Architecture
- Backbone: PointNet++ (PointNet2Encoder) with Set Abstraction modules
- Framework: OpenPoints/PointNeXt
- Defense: Shapley value-based desensitization for adversarial training
- Task: 3D point cloud classification (40 classes)
- Input: 1024 points × 3 coordinates
- Parameters: 1.47M
Results
| Metric | Value |
|---|---|
| ModelNet40 Val OA | 90.36% |
| ModelNet40 Test OA | 90.24% |
| Training Epochs | 300 |
| Batch Size | 192 |
Exported Formats
| Format | File | Use Case |
|---|---|---|
| PyTorch (.pth) | pytorch/desenat_v1.pth |
Training, fine-tuning |
| SafeTensors | pytorch/desenat_v1.safetensors |
Fast loading, safe |
| ONNX | N/A | Not exportable (custom CUDA FPS/ball_query ops) |
| TensorRT | Deferred | Generate on target hardware |
Custom CUDA Kernels
This module includes 5 optimized CUDA kernels (L4 sm_89):
- Chamfer Distance: 230x speedup over PyTorch CPU
- Fused Ball Query + Grouping: 318x speedup
- EMD Auction Algorithm: 2.4ms for 4×1024 points
- GPU Shapley Estimator: 0.38ms Monte Carlo estimation
- GPU Free-Form Deformation: 977x speedup over CPU PyGeM
Usage
import torch
from safetensors.torch import load_file
# Load weights
state_dict = load_file("pytorch/desenat_v1.safetensors")
# Build model (requires openpoints from DesenAT repo)
from openpoints.models import build_model_from_cfg
from openpoints.utils import EasyConfig
cfg = EasyConfig()
cfg.load("cfgs/modelnet40ply2048/pointnet++.yaml", recursive=True)
cfg.model.criterion_args = {"NAME": "CrossEntropy"}
model = build_model_from_cfg(cfg.model)
model.load_state_dict(state_dict)
model.eval()
# Inference
points = torch.randn(1, 1024, 3).cuda()
data = {"pos": points, "x": points.transpose(1, 2)}
logits = model(data) # (1, 40)
Training
- Hardware: NVIDIA L4 (23GB VRAM)
- Framework: PyTorch 2.11 + CUDA 12.8
- Optimizer: AdamW (lr=0.001, wd=0.0001)
- Scheduler: Warmup + Cosine decay
- Config: See
configs/baseline_st.toml
Defense Marketplace Value
Blue team counterpart to point cloud attacks. Hardens 3D perception against:
- Adversarial point perturbations
- Natural sensor corruptions (rain, fog, dust, vibration)
- Critical for autonomous vehicles and military ground platforms
License
Apache 2.0 — Robot Flow Labs / AIFLOW LABS LIMITED