2D Detector Research Articles

Calibration method and performance of a time-of-flight detector to measure absolute beam energy in proton therapy.

The beam energy is one of the most significant parameters in particle therapy since it is directly correlated to the particles' penetration depth inside the patient. Nowadays, the range accuracy is guaranteed by offline routine quality control checks mainly performed with water phantoms, 2D detectors with PMMA wedges, or multi-layer ionization chambers. The latter feature low sensitivity, slow collection time, and response dependent on external parameters, which represent limiting factors for the quality controls of beams delivered with fast energy switching modalities, as foreseen in future treatments. In this context, a device based on solid-state detectors technology, able to perform a direct and absolute beam energy measurement, is proposed as a viable alternative for quality assurance measurements and beam commissioning, paving the way for online range monitoring and treatment verification. This work follows the proof of concept of an energy monitoring system for clinical proton beams, based on Ultra Fast Silicon Detectors (featuring tenths of ps time resolution in 50μm active thickness, and single particle detection capability) and time-of-flight techniques. An upgrade of such a system is presented here, together with the description of a dedicated self-calibration method, proving that this second prototype is able to assess the mean particles energy of a monoenergetic beam without any constraint on the beam temporal structure, neither any a priori knowledge of the beam energy for the calibration of the system. A new detector geometry, consisting of sensors segmented in strips, has been designed and implemented in order to enhance the statistics of coincident protons, thus improving the accuracy of the measured time differences. The prototype was tested on the cyclotron proton beam of the Trento Protontherapy Center (TPC). In addition, a dedicated self-calibration method, exploiting the measurement of monoenergetic beams crossing the two telescope sensors for different flight distances, was introduced to remove the systematic uncertainties independently from any external reference. The novel calibration strategy was applied to the experimental data collected at TPC (Trento) and CNAO (Pavia). Deviations between measured and reference beam energies in the order of a few hundreds of keV with a maximum uncertainty of 0.5 MeV were found, in compliance with the clinically required water range accuracy of 1mm. The presented version of the telescope system, minimally perturbative of the beam, relies on a few seconds of acquisition time to achieve the required clinical accuracy and therefore represents a feasible solution for beam commission, quality assurance checks, and online beam energy monitoring.

FSFNet: Foreground Score-Aware Fusion for 3-D Object Detector Under Unfavorable Conditions

Nowadays, various multimodal fusion based 3D object detectors appear to provide a potential opportunity to solve the failure cases in single modality methods. However, current fusion approaches still face some unfavorable factors, e.g., poor illumination driving conditions and crowded traffic scenarios, which will cause unsatisfying image quality and objects occlusion. To this end, we present a multimodal fusion network FSFNet consisting of local graph-aware point cloud backbone, foreground score-aware fusion network (FSFN), and the proposals refining loss for 3D object detection task in this paper. Concretely, the directed graph is built to generate edge-wise features for each point, and the point features are supplemented with graph information in local graph-aware point cloud backbone. To alleviate the defect of undesirable image quality features caused by poor illumination condition, FSFN is designed to produce an adaptive multi-modal feature by taking point-wise foreground scores into consideration. Hence, level-wise point features with high confidence are fully utilized and the imperfect image information is suppressed in fusion stage. We further introduce proposals refining loss to reduce false positive and false negative cases in crowded scenes by optimizing the location and scores of predicted 3D bounding boxes. Extensive experiments conducted on KITTI benchmark demonstrate that FSFNet owns its superiority over state-of-the-art networks. Moreover, FSFN is verified to be robust against the image inputs under poor illumination conditions.

A 3D Drizzle Algorithm for JWST and Practical Application to the MIRI Medium Resolution Spectrometer

We describe an algorithm for application of the classic “drizzle” technique to produce 3D spectral cubes using data obtained from the slicer-type integral field unit (IFU) spectrometers on board the James Webb Space Telescope. This algorithm relies upon the computation of overlapping volume elements (composed of two spatial dimensions and one spectral dimension) between the 2D detector pixels and the 3D data cube voxels, and is greatly simplified by treating the spatial and spectral overlaps separately at the cost of just 0.03% in spectrophotometric fidelity. We provide a matrix-based formalism for the computation of spectral radiance, variance, and covariance from arbitrarily dithered data and comment on the performance of this algorithm for the Mid-Infrared Instrument’s Medium Resolution IFU Spectrometer. We derive a series of simplified scaling relations to account for covariance between cube spaxels in spectra extracted from such cubes, finding multiplicative factors ranging from 1.5–3 depending on the wavelength range and kind of data cubes produced. Finally, we discuss how undersampling produces periodic amplitude modulations in the extracted spectra in addition to those naturally produced by fringing within the instrument; reducing such undersampling artifacts below 1% requires a four-point dithering strategy and spectral extraction radii of 1.5 times the point-spread function FWHM or greater.

BEVDepth: Acquisition of Reliable Depth for Multi-View 3D Object Detection

In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work is based on a key observation -- depth estimation in recent approaches is surprisingly inadequate given the fact that depth is essential to camera 3D detection. Our BEVDepth resolves this by leveraging explicit depth supervision. A camera-awareness depth estimation module is also introduced to facilitate the depth predicting capability. Besides, we design a novel Depth Refinement Module to counter the side effects carried by imprecise feature unprojection. Aided by customized Efficient Voxel Pooling and multi-frame mechanism, BEVDepth achieves the new state-of-the-art 60.9% NDS on the challenging nuScenes test set while maintaining high efficiency. For the first time, the NDS score of a camera model reaches 60%. Codes have been released.

SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud

LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, they suffer from significant performance degeneration when facing unseen domains, such as different LiDAR configurations, different cities, and weather conditions. The mainstream approaches tend to solve these challenges by leveraging unsupervised domain adaptation (UDA) techniques. However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e.g., from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D), where only a few labeled target data is available, yet can significantly improve the adaptation performance. In particular, our SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the first stage, an Inter-domain Point-CutMix module is presented to efficiently align the point cloud distribution across domains. The Point-CutMix generates mixed samples of an intermediate domain, thus encouraging to learn domain-invariant knowledge. Then, in the second stage, we further enhance the model for better generalization on the unlabeled target set. This is achieved by exploring Intra-domain Point-MixUp in semi-supervised learning, which essentially regularizes the pseudo label distribution. Experiments from Waymo to nuScenes show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label. Our code is available at https://github.com/yinjunbo/SSDA3D.

Transformation-Equivariant 3D Object Detection for Autonomous Driving

3D object detection received increasing attention in autonomous driving recently. Objects in 3D scenes are distributed with diverse orientations. Ordinary detectors do not explicitly model the variations of rotation and reflection transformations. Consequently, large networks and extensive data augmentation are required for robust detection. Recent equivariant networks explicitly model the transformation variations by applying shared networks on multiple transformed point clouds, showing great potential in object geometry modeling. However, it is difficult to apply such networks to 3D object detection in autonomous driving due to its large computation cost and slow reasoning speed. In this work, we present TED, an efficient Transformation-Equivariant 3D Detector to overcome the computation cost and speed issues. TED first applies a sparse convolution backbone to extract multi-channel transformation-equivariant voxel features; and then aligns and aggregates these equivariant features into lightweight and compact representations for high-performance 3D object detection. On the highly competitive KITTI 3D car detection leaderboard, TED ranked 1st among all submissions with competitive efficiency. Code is available at https://github.com/hailanyi/TED.

Capability demonstration of a 3D CdZnTe detector on a high-altitude balloon flight

In collaboration between the University of Michigan and Los Alamos National Laboratory, a 3D position-sensing CdZnTe (CZT) detector prototype was built and integrated into a high-altitude balloon platform to evaluate its performance in a space-like mixed-radiation environment. The detector prototype, Orion Eagle, was designed to operate in near-vacuum environments without any temperature regulation. Orion Eagle was hand-launched from NASA’s Columbia Scientific Balloon Facility (CSBF) at Fort Sumner, NM on September 26, 2021, and successfully operated throughout a 9-hour flight, which reached 38.5 km in altitude. The flight met its objectives, successfully detecting atmospheric gamma rays and galactic cosmic rays, and raising the Technical Readiness Level from 4 to 6 for large-volume 3D CZT detector technology for space applications. Ionization tracks produced by charged particles create spatial signatures in the detector that are distinguishable from discrete gamma-ray interactions. Therefore, the 3D position-sensing capabilities using pixelated electrodes on a CZT detector can help enable discrimination of background charged particles from gamma-ray events without an anticoincidence shield. The potential for background rejection capability, ambient-temperature operation, gamma-ray coded-aperture and Compton imaging, and near High Purity Germanium (HPGe) energy resolution motivate the use of large-volume 3D CZT imaging spectrometers in future space missions.

MENet: Map-enhanced 3D object detection in bird’s-eye view for LiDAR point clouds

Three-dimensional (3D) object detection utilizes numerous onboard sensors to determine the position, size, and motion information of surrounding objects. Recently, some researchers have utilized HD maps in 3D object detection for LiDAR point clouds. However, existing LiDAR–map fusion detection methods simply take the HD map as an additional input and more prior information in the map is underexplored. To address this limitation, we employ the HD map in data augmentation for LiDAR 3D detection to eliminate the bias of distributions between the augmented training and test sets. Furthermore, we propose a map-enhanced 3D object detection network, called MENet, for LiDAR point clouds. Compared with the standard LiDAR-only detector in bird’s eye view (BEV), MENet extends LiDAR-based baselines by using a map encoder to encode the input rasterized HD map into a BEV feature. In addition, it uses a multimodal fuser to aggregate the map BEV feature with the LiDAR BEV feature extracted in the LiDAR stream. MENet achieved a mean average precision (mAP) of 56.90% and nuScenes detection score (NDS) of 63.43%, improving the mAP and NDS of its LiDAR-only backbone by 4.18% and 2.22%, respectively. Although the HD map is necessary at training time, the map-enhanced 3D detector can work robustly and perform better than the original baseline when there was no map input at test time. Experimental results further indicate that the proposed map plugins can improve the performance of both anchor-based and anchor-free models on different datasets. The code will be open-source at https://github.com/WHU-USI3DV/MENet.

High-throughput multimodal wide-field Fourier-transform Raman microscope

Raman microscopy is a powerful analytical technique for materials and life sciences that enables mapping the spatial distribution of the chemical composition of a sample. State-of-the-art Raman microscopes, based on point-scanning frequency-domain detection, have long (∼1s) pixel dwell times, making it challenging to acquire images of a significant area (e.g., 100×100µm). Here we present a compact wide-field Raman microscope based on a time-domain Fourier-transform approach, which enables parallel acquisition of the Raman spectra on all pixels of a 2D detector. A common-path birefringent interferometer with exceptional delay stability and reproducibility can rapidly acquire Raman maps (∼30min for a 250000pixel image) with high spatial (<1µm) and spectral (∼23cm−1) resolutions. Time-domain detection allows us to disentangle fluorescence and Raman signals, which can both be measured separately. We validate the system by Raman imaging plastic microbeads and demonstrate its multimodal operation by capturing fluorescence and Raman maps of a multilayer-WSe2 sample, providing complementary information on the strain and number of layers of the material.

A Lightweight and Detector-Free 3D Single Object Tracker on Point Clouds

Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking. However, it is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. In this paper, we address this issue by explicitly leveraging temporal motion cues and propose DMT, a Detector-free Motion-prediction-based 3D Tracking network that completely removes the usage of complicated 3D detectors and is lighter, faster, and more accurate than previous trackers. Specifically, the motion prediction module is first introduced to estimate a potential target center of the current frame in a point-cloud-free manner. Then, an explicit voting module is proposed to directly regress the 3D box from the estimated target center. Extensive experiments on KITTI and NuScenes datasets demonstrate that our DMT can still achieve better performance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 10% improvement over the NuScenes dataset) and a faster tracking speed (i.e., 72 FPS) than state-of-the-art approaches without applying any complicated 3D detectors. Our code is released at https://github.com/jimmy-dq/DMT.

SpatDistCalib: a GUI Python software for spatial-distortion correction of 2D detectors using splines.

CCD-based X-ray detector systems often suffer from spatial distortions. Reproducible distortions can be quantitatively measured with a calibration grid and described as a displacement matrix or as spline functions. The measured distortion can be used afterwards to undistort raw images or to refine the actual position of each pixel, e.g. for azimuthal integration. This article describes a method using a regular grid, not necessarily orthogonal, to measure the distortions. The graphical user interface (GUI) Python software that is used to implement this method is available under a GPLv3 license on ESRF GitLab, and produces a spline file that is usable with data-reduction software such as FIT2D or pyFAI.

ProUDA: Progressive unsupervised data augmentation for semi-Supervised 3D object detection on point cloud

Unsupervised data augmentation (UDA) is a simple and general semi-supervised learning (SSL) framework. However, for the task of semi-supervised 3D object detection (SSOD-3D), due to the impact of object occlusion and point cloud resolution, the quality of pseudo labels is uncertain so that the performance of UDA is limited. In this paper, We propose an efficient and novel SSL framework progressive unsupervised data augmentation (ProUDA). At first, to minimize the overfitting risk of the inaccurate pseudo labels, we sort the unlabeled samples by prediction complexity, and present a progressive consistency loss to adjust the usage ratio of the unlabeled samples. After that, we employ a strategy of iteration check to select the best learning result using a few but representative validation dataset annotated from the unlabeled samples. It ensures the safe model weight updating. Extensive experiments are conducted on both the public indoor and outdoor 3D object detection datasets. Results demonstrate that ProUDA has better 3D average precision than UDA and the proposed method benefits to 3D object detector training.

A Multimodal 3-D Detector With Attention From the Corresponding Modal

Robust 3D detection based on camera-LiDAR fusion has become a research focus for autonomous driving. Despite the extensive efforts invested, it remains an extremely challenging task to effectively establish fine-grained corresponding relations between different modalities and achieve complementary multimodal fusion free of interference. To these ends, we propose a multimodal 3D detector termed as CFFNet, mainly consisting of a multimodal feature extraction backbone network and a corresponding feature-based attention fusion (CFAF) module. Specifically, the multimodal feature extraction backbone is devised to build a simple yet effective cross-modal correspondence by unifying multimodal features into a shared bird’s eye view (BEV) space. Expressive and noise-suppressed image BEV features are generated from pseudo image voxels, whose depth information is estimated with range-wise attention drawn from LiDAR BEV features. With the prominent heterogeneity of multimodal features taken into consideration, the CFAF module performs a discriminative fusion targeted at informative and contributive features retained in different modalities. The fusion is accomplished by element-wisely reweighting image BEV features with attention generated from differential features of LiDAR. The multimodal feature extraction backbone and CFAF module are both evaluated and proved to offer significant performance gains separately and together. Evaluations conducted on the KITTI 3D object detection dataset show that the proposed CFFNet achieves state-of-the-art performance.

Real-Time Stereo 3D Car Detection With Shape-Aware Non-Uniform Sampling

Pseudo-LiDAR based stereo 3D detectors have gained popularity due to their high accuracy. However, these methods need dense depth supervision and suffer from inferior speed. To solve these two issues, a recently introduced RTS3D builds an efficient 4D feature-consistency embedding (FCE) space for the object intermediate representation without depth supervision, where FCE space performs uniform sampling to generate feature sampling points, which ignores the importance of different object regions. In this paper, we observe that, compared with the inner region, the outer region of the object plays a more important role for accurate 3D detection. To fully exploit the useful information from the outer region, we propose a novel shape-aware non-uniform sampling strategy. Instead of uniform sampling, our proposed non-uniform sampling strategy performs dense sampling in outer region and sparse sampling in inner region. Therefore, more points are sampled from the outer region and more useful features are extracted for 3D detection. In addition, we design a high-level semantic enhanced FCE module to exploit more contextual information and suppress noise better. As a result, it further improves feature discrimination of each sampling point. Experimental results on the KITTI dataset show the effectiveness of the proposed method. Compared with the baseline RTS3D, our proposed method has 2.57% improvement on car AP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{3d}$ </tex-math></inline-formula> almost without extra network parameters. Moreover, our proposed method outperforms the state-of-the-art methods without extra supervision at a real-time speed.

Synthesizing pseudo-Kossel lines from neutron transmission data. II. Validation with neutron diffraction data

A new method to recover the orientation matrix of a single crystal with a known unit cell by analyzing synthesized pseudo-Kossel lines from time-of-flight neutron transmission data has been outlined in a companion article [Dessieux et al. (2023). J. Appl. Cryst. 56, https://dx.doi.org/10.1107/S1600576723001346; referred to here as Article I]. In this work, validation of this new technique is presented by employing experimental neutron transmission and diffraction measurements performed on two copper single-crystal specimens. Time-of-flight spectra were recorded during rotation (ω) of the single crystals about a vertical axis perpendicular to the incident neutron beam. The λ–ω maps recorded in transmission are utilized to determine the crystal orientation with respect to the neutron beam, following the procedure presented in Article I. To validate the indexing procedure, the crystal orientations are compared with those obtained via conventional methods using the diffraction data. The resulting pseudo-Kossel lines across the 2D detectors are also observed for the first time.

FusionRCNN: LiDAR-Camera Fusion for Two-Stage 3D Object Detection

Accurate and reliable perception systems are essential for autonomous driving and robotics. To achieve this, 3D object detection with multi-sensors is necessary. Existing 3D detectors have significantly improved accuracy by adopting a two-stage paradigm that relies solely on LiDAR point clouds for 3D proposal refinement. However, the sparsity of point clouds, particularly for faraway points, makes it difficult for the LiDAR-only refinement module to recognize and locate objects accurately. To address this issue, we propose a novel multi-modality two-stage approach called FusionRCNN. This approach effectively and efficiently fuses point clouds and camera images in the Regions of Interest (RoI). The FusionRCNN adaptively integrates both sparse geometry information from LiDAR and dense texture information from the camera in a unified attention mechanism. Specifically, FusionRCNN first utilizes RoIPooling to obtain an image set with a unified size and gets the point set by sampling raw points within proposals in the RoI extraction step. Then, it leverages an intra-modality self-attention to enhance the domain-specific features, followed by a well-designed cross-attention to fuse the information from two modalities. FusionRCNN is fundamentally plug-and-play and supports different one-stage methods with almost no architectural changes. Extensive experiments on KITTI and Waymo benchmarks demonstrate that our method significantly boosts the performances of popular detectors. Remarkably, FusionRCNN improves the strong SECOND baseline by 6.14% mAP on Waymo and outperforms competing two-stage approaches.

3D Multi-Object Tracking Based on Dual-Tracker and D-S Evidence Theory

Most of the current self-driving cars are equipped with a variety of sensors (such as lidar, camera), and it has become a trend to integrate multi-sensor information to achieve 3D multi-object tracking (MOT). In addition, unlike the perfect experimental conditions on public datasets, cars on real roads may experience a 3D sensor failure due to weather, mutual interference, etc. In this paper, we propose a dual-tracker-based 3D MOT system, which fuses 2D and 3D information to track objects and can still ensure good tracking accuracy when the 3D sensor fails in a single frame or multiple consecutive frames. Among them, two internal trackers complete the data association of 3D and 2D information, respectively. However, the outputs of the two internal trackers may conflict. First, we calculate the degree of association of potential matched pairs from the features extracted by each sensor, and then normalize it into the mass function of D-S evidence theory. Finally, we combine the evidence to obtain the final matched pairs of detection instances and track estimates. We do extensive experiments on the KITTI MOT dataset and simulate sensor failure scenarios by ignoring the output of the 3D detector. Using the latest evaluation measure for comparison, the results show that our method outperforms other advanced open-source 3D MOTs in a variety of scenarios.

Design and Simulated Electrical Properties of a Proposed Implanted-Epi Silicon 3D-Spherical Electrode Detector

A new type of 3D electrode detector, named here as the Implanted-Epi Silicon 3D-Spherical Electrode Detector, is proposed in this work. Epitaxial and ion implantation processes can be used in this new detector, allowing bowl-shaped electrodes to penetrate the silicon completely. The distance between the bowl cathode and the central collection electrode is basically the same, thus the total depletion voltage of Implanted-Epi Silicon 3D-Spherical Electrode Detectors is no longer directively correlated with the thickness of the silicon wafer, but only related to the electrode spacing. In this work, we model the device physics of this new structure and use a simulation program to conduct a systematic 3D simulation of its electrical characteristics, including electric potential and electric field distributions, electron concentration profile, leakage current, and capacitance, and compare it to the traditional 3D detectors. The theoretical and simulation study found that the internal electric potential of the new detector was smooth and no potential saddle point was found. The electric field is also uniform, and there is no zero field and a low electric field area. Compared with the traditional silicon 3D electrode detectors, the full depletion voltage is greatly reduced and the charge collection efficiency is improved. As a large electrode spacing (up to 500 μm) can be realized in the Implanted-Epi Silicon 3D-Spherical Electrode Detector thanks to their advantage of a greatly reduced full depletion voltage, detectors with large pixel cells (and thus small dead volume) can be developed for applications in photon science (X-ray, among others).

Positional correction of a 3D position-sensitive virtual Frisch-grid CZT detector for gamma spectroscopy and imaging based on a theoretical assumption

The virtual Frisch-grid method for room-temperature radiation detectors has been widely used because of its simplicity and high performance. Recently, side electrodes were separately attached to each surface of the detectors instead of covering the entire detector surface with a single electrode. The side-electrode structure enables the measurement of the three-dimensional (3D) gamma-ray interaction in the detector. The positional information of the interaction can then be utilized to precisely calibrate the response of the detector for gamma-ray spectroscopy and imaging. In this study, we developed a 3D position-sensitive 5 × 5 × 12 mm3 cadmium-zinc-telluride (CZT) detector and applied a flattening method to correct detector responses. Collimated gamma-rays incident on the surface of the detector were scanned to evaluate the positional accuracy of the detection system. Positional distributions of the radiation interactions with the detector were imaged for quantitative and qualitative evaluation. The energy spectra of various radioisotopes were measured and improved by the detector response calibration according to the calculated positional information. The energy spectra ranged from 59.5 keV (emitted by 241Am) to 1332 keV (emitted by 60Co). The best energy resolution was 1.06% at 662 keV when the CZT detector was voxelized to 20 × 20 × 10.

X-ray stress analysis with a 2D detector. Part 1. Data acquisition and treatment strategies

In the field of stress analysis via X-ray diffraction from polycrystalline specimens, the use of a 2D detector allows the operator to break free from the equatorial plane and devise new acquisition and treatment strategies for both synchrotron facilities and in-house laboratory equipment. Most of the time, however, the data acquired with these detectors are treated through radial–azimuthal binning in order to go back to classical pattern fitting strategies. The aim of the present paper is to propose a new approach to these issues, called RingFit-2D. First, it is shown that the set of angles used classically is no longer adapted to 2D detectors and a new set is proposed. A geometric description of the setup is then used to perform a global fitting of the images and to show that some issues linked to the spatial calibration of the detector can be easily detected through this approach. It is also shown that some specific formulations proposed in the literature can be derived from the general formulation described in the present paper. The second part of this work [François (2023). J. Appl. Cryst. 56, 61–70] will deal with the modelling and quantification of alignment errors in the diffractometer.