Single-photon Light Detection and Ranging (LiDAR) systems are often equipped
with an array of detectors for improved spatial resolution and sensing speed.
However, given a fixed amount of flux produced by the laser transmitter across
the scene, the per-pixel Signal-to-Noise Ratio (SNR) will decrease when more
pixels are packed in a unit space. This presents a fundamental trade-off
between the spatial resolution of the sensor array and the SNR received at each
pixel. Theoretical characterization of this fundamental limit is explored. By
deriving the photon arrival statistics and introducing a series of new
approximation techniques, the Mean Squared Error (MSE) of the
maximum-likelihood estimator of the time delay is derived. The theoretical
predictions align well with simulations and real data.

单光子激光雷达系统的关键性能 —— 光信号在传感器像素上的信噪比与空间分辨率之间的权衡被研究和理论分析。

单光子 LiDAR 的分辨限制

Resolution Limit of Single-Photon LiDAR

In recent years, the field of autonomous driving has witnessed remarkable
advancements, driven by the integration of a multitude of sensors, including
cameras and LiDAR systems, in different prototypes. However, with the
proliferation of sensor data comes the pressing need for more sophisticated
information processing techniques. This research paper introduces a novel
modification to an object detection network that uses camera and lidar
information, incorporating an additional branch designed for the task of
re-identifying objects across adjacent cameras within the same vehicle while
elevating the quality of the baseline 3D object detection outcomes. The
proposed methodology employs a two-step detection pipeline: initially, an
object detection network is employed, followed by a 3D box estimator that
operates on the filtered point cloud generated from the network's detections.
Extensive experimental evaluations encompassing both 2D and 3D domains validate
the effectiveness of the proposed approach and the results underscore the
superiority of this method over traditional Non-Maximum Suppression (NMS)
techniques, with an improvement of more than 5\% in the car category in the
overlapping areas.

近年来，自动驾驶领域取得了显著进展，本文提出一种新颖的修改方法，利用摄像机和激光雷达信息，将额外的分支整合到目标检测网络中，用于在同一车辆中的相邻摄像头之间重新识别物体，同时提高基线 3D 目标检测结果的质量。经过广泛实验验证，该方法在 2D 和 3D 领域的有效性得到证实，结果表明该方法优于传统的非最大抑制（NMS）技术，在重叠区域的车辆类别中改进超过 5%。

三维障碍多摄像头系统的联合目标检测与重新识别

Joint object detection and re-identification for 3D obstacle  multi-camera systems

Learning signed distance functions (SDFs) from 3D point clouds is an
important task in 3D computer vision. However, without ground truth signed
distances, point normals or clean point clouds, current methods still struggle
from learning SDFs from noisy point clouds. To overcome this challenge, we
propose to learn SDFs via a noise to noise mapping, which does not require any
clean point cloud or ground truth supervision for training. Our novelty lies in
the noise to noise mapping which can infer a highly accurate SDF of a single
object or scene from its multiple or even single noisy point cloud
observations. Our novel learning manner is supported by modern Lidar systems
which capture multiple noisy observations per second. We achieve this by a
novel loss which enables statistical reasoning on point clouds and maintains
geometric consistency although point clouds are irregular, unordered and have
no point correspondence among noisy observations. Our evaluation under the
widely used benchmarks demonstrates our superiority over the state-of-the-art
methods in surface reconstruction, point cloud denoising and upsampling. Our
code, data, and pre-trained models are available at
this https URL

本论文提出通过噪声到噪声的映射来学习带符号距离函数，并利用现代 Lidar 系统拍摄的多个嘈杂观测来推断单个物体或场景的高度准确的 SDF，实现表面重建、点云去噪和上采样等任务，并在公开的基准测试中表现出比现有方法更优秀的性能。

通过噪声到噪声映射从有噪音 3D 点云学习符号距离函数

Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise  to Noise Mapping

Single photon avalanche diodes (SPADs) are starting to play a pivotal role in
the development of photon-efficient, long-range LiDAR systems. However, due to
non-linearities in their image formation model, a high photon flux (e.g., due
to strong sunlight) leads to distortion of the incident temporal waveform, and
potentially, large depth errors. Operating SPADs in low flux regimes can
mitigate these distortions, but, often requires attenuating the signal and
thus, results in low signal-to-noise ratio. In this paper, we address the
following basic question: what is the optimal photon flux that a SPAD-based
LiDAR should be operated in? We derive a closed form expression for the optimal
flux, which is quasi-depth-invariant, and depends on the ambient light
strength. The optimal flux is lower than what a SPAD typically measures in real
world scenarios, but surprisingly, considerably higher than what is
conventionally suggested for avoiding distortions. We propose a simple,
adaptive approach for achieving the optimal flux by attenuating incident flux
based on an estimate of ambient light strength. Using extensive simulations and
a hardware prototype, we show that the optimal flux criterion holds for several
depth estimators, under a wide range of illumination conditions.

本文提出一种操作基于 SPAD 的 LiDAR 所需的最佳光子通量，通过衰减入射通量来实现最佳通量，该方法在大量模拟和硬件原型实验中得到验证。