3D object detection using point cloud (PC) data is vital for autonomous
driving perception pipelines, where efficient encoding is key to meeting
stringent resource and latency requirements. PointPillars, a widely adopted
bird's-eye view (BEV) encoding, aggregates 3D point cloud data into 2D pillars
for high-accuracy 3D object detection. However, most state-of-the-art methods
employing PointPillar overlook the inherent sparsity of pillar encoding,
missing opportunities for significant computational reduction. In this study,
we propose a groundbreaking algorithm-hardware co-design that accelerates
sparse convolution processing and maximizes sparsity utilization in
pillar-based 3D object detection networks. We investigate sparsification
opportunities using an advanced pillar-pruning method, achieving an optimal
balance between accuracy and sparsity. We introduce PillarAcc, a
state-of-the-art sparsity support mechanism that enhances sparse pillar
convolution through linear complexity input-output mapping generation and
conflict-free gather-scatter memory access. Additionally, we propose dataflow
optimization techniques, dynamically adjusting the pillar processing schedule
for optimal hardware utilization under diverse sparsity operations. We evaluate
PillarAcc on various cutting-edge 3D object detection networks and benchmarks,
achieving remarkable speedup and energy savings compared to representative edge
platforms, demonstrating record-breaking PointPillars speed of 500FPS with
minimal compromise in accuracy.

本研究提出了一种具有开创性的算法硬件协同设计，利用 PointPillars 编码的稀疏性加速 3D 目标检测网络的处理，达到了极高的节能与速度。

PillarAcc：边缘设备上用于实时点云 3D 物体检测的稀疏 PointPillars 加速器

PillarAcc: Sparse PointPillars Accelerator for Real-Time Point Cloud 3D  Object Detection on Edge Devices

Object detection in point clouds is an important aspect of many robotics
applications such as autonomous driving. In this paper we consider the problem
of encoding a point cloud into a format appropriate for a downstream detection
pipeline. Recent literature suggests two types of encoders; fixed encoders tend
to be fast but sacrifice accuracy, while encoders that are learned from data
are more accurate, but slower. In this work we propose PointPillars, a novel
encoder which utilizes PointNets to learn a representation of point clouds
organized in vertical columns (pillars). While the encoded features can be used
with any standard 2D convolutional detection architecture, we further propose a
lean downstream network. Extensive experimentation shows that PointPillars
outperforms previous encoders with respect to both speed and accuracy by a
large margin. Despite only using lidar, our full detection pipeline
significantly outperforms the state of the art, even among fusion methods, with
respect to both the 3D and bird's eye view KITTI benchmarks. This detection
performance is achieved while running at 62 Hz: a 2 - 4 fold runtime
improvement. A faster version of our method matches the state of the art at 105
Hz. These benchmarks suggest that PointPillars is an appropriate encoding for
object detection in point clouds.

本篇论文介绍了一种新型编码器 PointPillars，该编码器可以将点云转为适合下游检测通道的格式，可应用于众多机器人应用中的物体检测，证明其在速度和准确性上都优于现有编码器，可优化无人驾驶等应用领域。