提升3D目标检测效率，解决稀疏网络长距离感知弱、特征丢失问题。
FSHNet: Fully Sparse Hybrid Network for 3D Object Detection
- 用槽式分块增强稀疏体素的长程特征提取能力
- 在Waymo等数据集上实现0.9%~1.7%的精度提升
- 适合追求高效高精度3D检测的研究者与工程师
全稀疏3D检测器因在远距离检测中效率高而备受关注。然而，现有稀疏检测器仅从非空体素提取特征，削弱了长距离交互能力并导致中心特征缺失，前者降低特征提取性能，后者阻碍网络优化。为此，本文提出全稀疏混合网络FSHNet。FSHNet引入新型槽式分块（SlotFormer）模块，通过槽式划分稀疏体素，相比传统窗口划分具有更大感受野，显著增强长程特征提取能力。同时，提出动态稀疏标签分配策略，提供更多高质量正样本以深化网络优化。为进一步提升性能，设计稀疏上采样模块，重构下采样体素，保留小目标检测至关重要的细粒度细节。在Waymo、nuScenes和Argoverse2基准上的大量实验验证了FSHNet的有效性。代码已开源。
原文摘要 · Abstract (English)
Fully sparse 3D detectors have recently gained significant attention due to their efficiency in long-range detection. However, sparse 3D detectors extract features only from non-empty voxels, which impairs long-range interactions and causes the center feature missing. The former weakens the feature extraction capability, while the latter hinders network optimization. To address these challenges, we introduce the Fully Sparse Hybrid Network (FSHNet). FSHNet incorporates a proposed SlotFormer block to enhance the long-range feature extraction capability of existing sparse encoders. The SlotFormer divides sparse voxels using a slot partition approach, which, compared to traditional window partition, provides a larger receptive field. Additionally, we propose a dynamic sparse label assignment strategy to deeply optimize the network by providing more high-quality positive samples. To further enhance performance, we introduce a sparse upsampling module to refine downsampled voxels, preserving fine-grained details crucial for detecting small objects. Extensive experiments on the Waymo, nuScenes, and Argoverse2 benchmarks demonstrate the effectiveness of FSHNet. The code is available at https://github.com/Say2L/FSHNet.
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