## 2 Related Works
TED [12] initially employs SpConv to extract transformation-equivariant voxel features and then aligns and aggregates these features into a lightweight representation, thereby reducing computational costs. Traditional methods often apply VFE to single-frame point clouds, while DynStaF [13] incorporates a branch of multi-frame VFE, capitalizing on the rich semantic information of multi-frame data and the precise positional information of single-frame data to enhance the localization accuracy of bounding boxes. DSVT [14] innovates the backbone network, employing a transformer-based backbone with Dynamic Sparse Window Attention to process sparse voxels in parallel, and proposing a learnable 3D pooling operation to effectively encode spatial information, effectively improving detection performance. Voxel-based methods can achieve high detection accuracy, but the existence of a large number of empty voxels necessitates the consumption of significant memory space. Conversely, pillar-based methods significantly reduce the number of empty voxels, resulting in higher inference speeds. The pillar-based approach [15; 16; 17; 18; 19; 20] generally encodes point clouds into 2D pillars, which are then further processed by 2D backbones to extract features and recognize objects. PointPillars [15] and PillarNeXt [16] initially transform point clouds into pillars and subsequently convert these pillars into pseudo images. After this conversion, PointPillars utilizes RPN [25] and PillarNeXt employs a ResNet-based [26] backbone to extract 2D features. The detection paradigm proposed by PointPillars significantly accelerates the speed of point cloud object recognition, marking a milestone in the field. The creators of PillarNet [17] designed a simple encoder based on 2D detection prior to the Detection Neck, enabling the extraction of sparse pillar features and achieving high precision and efficiency in recognition tasks. Pillar R-CNN [18] translates the pillar representation into a Bird's Eye View (BEV) representation and employs RPN [25] to generate object proposals, followed by refinement to identify objects. In summary, grid-based methods for point cloud processing have evolved significantly, with voxel-based approaches offering higher accuracy at the cost of increased computational resources, and with pillar-based methods providing a faster alternative with slightly lower precision. The continuous advancements in these techniques contribute to the growing capabilities in the field of point cloud object recognition. **Point cloud recognition for distant and small objects**. Due to the sparsity of point clouds, it is challenging to accurately recognize distant and small objects. Many scholars have conducted research to address this issue. TimePillars [19] introduces a memory unit between the 2D CNN and the Detection Head. This memory unit is composed of convolutional Gated Recurrent Units (GRUs), which enhances the recognition accuracy of distant objects. FastPillars [20] utilizes a Maximum-Attention Pillar Encoding (MAPE) approach. MAPE first encodes points, then utilizes max-pooling encoding to aggregate features within pillars, then employs attention-pooling encoding to capture fine-grained features, and finally merges the two. This method effectively improves recognition capability for small objects. Ref. [21] introduces a Fully Sparse Object Detector (FSD), proposing Sparse Instance Recognition (SIR) to address the issue of vanishing central features in sparse feature maps. Additionally, an enhanced version of FSD called FSD++ is proposed which utilizes temporal information to eliminate data redundancy and combines information from multiple frames to form a Super-Sparse Input. Experiments demonstrate that both FSD and FSD++ possess strong distant recognition capabilities.