标题： 基于单光子LiDAR的概率点云的鲁棒三维目标检测 显示英文标题

标题： Robust 3D Object Detection using Probabilistic Point Clouds from Single-Photon LiDARs

摘要： 基于LiDAR的3D传感器提供点云，这是一种常用的3D表示方法，用于各种场景理解任务。 现代LiDAR在一些现实场景中面临关键挑战，例如远距离或低反照率物体，这会导致点云稀疏或错误。 这些错误源于噪声的原始LiDAR测量，并会传播到下游感知模型中，导致准确性的严重损失。 这是因为传统的3D处理流程在构建点云时不会保留任何来自原始测量的不确定性信息。 我们提出了概率点云（PPC），一种新的3D场景表示方法，其中每个点都附加了一个概率属性，该属性概括了原始数据中的测量不确定性（或置信度）。 我们进一步引入了利用PPC进行鲁棒3D目标检测的推理方法；这些方法具有通用性，可以作为计算轻量级的模块集成到3D推理流程中。 我们通过仿真和真实采集证明，基于PPC的3D推理方法在涉及小物体、远距离物体、低反照率物体以及强环境光的挑战性室内外场景中，优于使用LiDAR的多个基线方法以及相机-LiDAR融合模型。 我们的项目网页位于https://bhavyagoyal.github.io/ppc 。 显示英文摘要

摘要： LiDAR-based 3D sensors provide point clouds, a canonical 3D representation used in various scene understanding tasks. Modern LiDARs face key challenges in several real-world scenarios, such as long-distance or low-albedo objects, producing sparse or erroneous point clouds. These errors, which are rooted in the noisy raw LiDAR measurements, get propagated to downstream perception models, resulting in potentially severe loss of accuracy. This is because conventional 3D processing pipelines do not retain any uncertainty information from the raw measurements when constructing point clouds. We propose Probabilistic Point Clouds (PPC), a novel 3D scene representation where each point is augmented with a probability attribute that encapsulates the measurement uncertainty (or confidence) in the raw data. We further introduce inference approaches that leverage PPC for robust 3D object detection; these methods are versatile and can be used as computationally lightweight drop-in modules in 3D inference pipelines. We demonstrate, via both simulations and real captures, that PPC-based 3D inference methods outperform several baselines using LiDAR as well as camera-LiDAR fusion models, across challenging indoor and outdoor scenarios involving small, distant, and low-albedo objects, as well as strong ambient light. Our project webpage is at https://bhavyagoyal.github.io/ppc .