Real-time and high-performance 3D object detection plays a critical role in
autonomous driving and robotics. Recent pillar-based 3D object detectors have
gained significant attention due to their compact representation and low
computational overhead, making them suitable for onboard deployment and
quantization. However, existing pillar-based detectors still suffer from
information loss along height dimension and large numerical distribution
difference during pillar feature encoding (PFE), which severely limits their
performance and quantization potential. To address above issue, we first unveil
the importance of different input information during PFE and identify the
height dimension as a key factor in enhancing 3D detection performance.
Motivated by this observation, we propose a height-aware pillar feature encoder
named PillarHist. Specifically, PillarHist statistics the discrete distribution
of points at different heights within one pillar. This simple yet effective
design greatly preserves the information along the height dimension while
significantly reducing the computation overhead of the PFE. Meanwhile,
PillarHist also constrains the arithmetic distribution of PFE input to a stable
range, making it quantization-friendly. Notably, PillarHist operates
exclusively within the PFE stage to enhance performance, enabling seamless
integration into existing pillar-based methods without introducing complex
operations. Extensive experiments show the effectiveness of PillarHist in terms
of both efficiency and performance.

实时高性能的三维物体检测在自动驾驶和机器人领域起着关键作用，本文提出了一种名为 PillarHist 的高度感知柱状特征编码器，通过对不同高度内的点的离散分布进行统计，显著提高了信息保存和计算效率的能力，并且对于 PFE 输入的算术分布进行了约束，使之有利于量化，实验证明了 PillarHist 在效率和性能方面的有效性。