SST: Single-stride Sparse Transformer

This is the official implementation of paper:

Embracing Single Stride 3D Object Detector with Sparse Transformer

Lue Fan,
Ziqi Pang,
Tianyuan Zhang,
Yu-Xiong Wang,
Hang Zhao,
Feng Wang,
Naiyan Wang,
Zhaoxiang Zhang

Paper Link (Check again on Monday)

Introduction and Highlights

  • SST is a single-stride network, which maintains original feature resolution from the beginning to the end of the network. Due to the characterisric of single stride, SST achieves exciting performances on small object detection (Pedestrian, Cyclist).
  • For simplicity, except for backbone, SST is almost the same with the basic PointPillars in MMDetection3D. With such a basic setting, SST achieves state-of-the-art performance in Pedestrian and Cyclist and outperforms PointPillars more than 10 AP only at a cost of 1.5x latency.
  • SST consists of 6 Regional Sparse Attention (SRA) blocks, which deal with the sparse voxel set. It’s similar to Submanifold Sparse Convolution (SSC), but much more powerful than SSC. It’s locality and sparsity guarantee the efficiency in the single stride setting.
  • The SRA can also be used in many other task to process sparse point clouds. Our implementation of SRA only relies on the pure Python APIs in PyTorch without engineering efforts
    as taken in the CUDA implementation of sparse convolution.
  • Large room for further improvements. For example, second stage, anchor-free head, IoU scores and advanced techniques from ViT, etc.


PyTorch >= 1.9 is highly recommended for a better support of the checkpoint technique.

Our immplementation is based on MMDetection3D, so just follow their getting_started and simply run the script: Then you will get a basic results of SST after 5~7 hours (depends on your devices).

We only provide the single-stage model here, as for our two-stage models, please follow LiDAR-RCNN. It’s also a good choice to apply other powerful second stage detectors to our single-stage SST.

Main results

Single-stage Model (based on PointPillars) on Waymo validation split

#Sweeps Veh_L1 Ped_L1 Cyc_L1
SST_1f 1 73.57 80.01 70.72
SST_3f 3 75.16 83.24 75.96

Note that we train the 3 classes together, so the performance above is a little bit lower than that reported in our paper.


  • Build SRA block with similar API as Sparse Convolution for more convenient usage.


This project is based on the following codebases.


View Github