VRSO: Visual-Centric Reconstruction for Static Object Annotation

As a part of the perception results of intelligent driving systems, static object detection (SOD) in 3D space provides crucial cues for driving environment understanding. With the rapid deployment of deep neural networks for SOD tasks, the demand for high-quality training samples soars. The traditional, also reliable, way is manual labeling over the dense LiDAR point clouds and reference images. Though most public driving datasets adopt this strategy to provide SOD ground truth (GT), it is still expensive (requires LiDAR scanners) and low-efficient (time-consuming and unscalable) in practice. This paper introduces VRSO, a visual-centric approach for static object annotation. VRSO is distinguished in low cost, high efficiency, and high quality: (1) It recovers static objects in 3D space with only camera images as input, and (2) manual labeling is barely involved since GT for SOD tasks is generated based on an automatic reconstruction and annotation pipeline. (3) Experiments on the Waymo Open Dataset show that the mean reprojection error from VRSO annotation is only 2.6 pixels, around four times lower than the Waymo labeling (10.6 pixels). Source code is available at: https://github.com/CaiYingFeng/VRSO.

Night-Rider: Nocturnal Vision-aided Localization in Streetlight Maps Using Invariant Extended Kalman Filtering

Vision-aided localization for low-cost mobile robots in diverse environments has attracted widespread attention recently. Although many current systems are applicable in daytime environments, nocturnal visual localization is still an open problem owing to the lack of stable visual information. An insight from most nocturnal scenes is that the static and bright streetlights are reliable visual information for localization. Hence we propose a nocturnal vision-aided localization system in streetlight maps with a novel data association and matching scheme using object detection methods. We leverage the Invariant Extended Kalman Filter (InEKF) to fuse IMU, odometer, and camera measurements for consistent state estimation at night. Furthermore, a tracking recovery module is also designed for tracking failures. Experiments on multiple real nighttime scenes validate that the system can achieve remarkably accurate and robust localization in nocturnal environments.

AscDAMs: Advanced SLAM-based channel detection and mapping system

Obtaining high-resolution, accurate channel topography and deposit conditions is the prior challenge for the study of channelized debris flow. Currently, wide-used mapping technologies including satellite imaging and drone photogrammetry struggle to precisely observe channel interior conditions of mountainous long-deep gullies, particularly those in the Wenchuan Earthquake region. SLAM is an emerging tech for 3D mapping; however, extremely rugged environment in long-deep gullies poses two major challenges even for the state-of-art SLAM: (1) Atypical features; (2) Violent swaying and oscillation of sensors. These issues result in large deviation and lots of noise for SLAM results. To improve SLAM mapping in such environments, we propose an advanced SLAM-based channel detection and mapping system, namely AscDAMs. It features three main enhancements to post-process SLAM results: (1) The digital orthophoto map aided deviation correction algorithm greatly eliminates the systematic error; (2) The point cloud smoothing algorithm substantially diminishes noises; (3) The cross section extraction algorithm enables the quantitative assessment of channel deposits and their changes. Two field experiments were conducted in Chutou Gully, Wenchuan County in China in February and November 2023, representing observations before and after the rainy season. We demonstrate the capability of AscDAMs to greatly improve SLAM results, promoting SLAM for mapping the specially challenging environment. The proposed method compensates for the insufficiencies of existing technologies in detecting debris flow channel interiors including detailed channel morphology, erosion patterns, deposit distinction, volume estimation and change detection. It serves to enhance the study of full-scale debris flow mechanisms, long-term post-seismic evolution, and hazard assessment.

Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies

We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter fNL. Our sample comprises over 12 million targets, covering 14,000 square degrees of the sky, with redshifts in the range 0.2< z < 1.35. We identify Galactic extinction, survey depth, and astronomical seeing as the primary sources of systematic error, and employ linear regression and artificial neural networks to alleviate non-cosmological excess clustering on large scales. Our methods are tested against log-normal simulations with and without fNL and systematics, showing superior performance of the neural network treatment in reducing remaining systematics. Assuming the universality relation, we find fNL $= 47^{+14(+29)}_{-11(-22)}$ at 68\%(95\%) confidence. With a more aggressive treatment, including regression against the full set of imaging maps, our maximum likelihood value shifts slightly to fNL$ \sim 50$ and the uncertainty on fNL increases due to the removal of large-scale clustering information. We apply a series of robustness tests (e.g., cuts on imaging, declination, or scales used) that show consistency in the obtained constraints. Despite extensive efforts to mitigate systematics, our measurements indicate fNL > 0 with a 99.9 percent confidence level. This outcome raises concerns as it could be attributed to unforeseen systematics, including calibration errors or uncertainties associated with low-\ell systematics in the extinction template. Alternatively, it could suggest a scale-dependent fNL model--causing significant non-Gaussianity around large-scale structure while leaving cosmic microwave background scales unaffected. Our results encourage further studies of fNL with DESI spectroscopic samples, where the inclusion of 3D clustering modes should help separate imaging systematics.

Why semantics matters: A deep study on semantic particle-filtering localization in a LiDAR semantic pole-map

In most urban and suburban areas, pole-like structures such as tree trunks or utility poles are ubiquitous. These structural landmarks are very useful for the localization of autonomous vehicles given their geometrical locations in maps and measurements from sensors. In this work, we aim at creating an accurate map for autonomous vehicles or robots with pole-like structures as the dominant localization landmarks, hence called pole-map. In contrast to the previous pole-based mapping or localization methods, we exploit the semantics of pole-like structures. Specifically, semantic segmentation is achieved by a new mask-range transformer network in a mask-classfication paradigm. With the semantics extracted for the pole-like structures in each frame, a multi-layer semantic pole-map is created by aggregating the detected pole-like structures from all frames. Given the semantic pole-map, we propose a semantic particle-filtering localization scheme for vehicle localization. Theoretically, we have analyzed why the semantic information can benefit the particle-filter localization, and empirically it is validated on the public SemanticKITTI dataset that the particle-filtering localization with semantics achieves much better performance than the counterpart without semantics when each particle's odometry prediction and/or the online observation is subject to uncertainties at significant levels.

GSB: Group Superposition Binarization for Vision Transformer with Limited Training Samples

Affected by the massive amount of parameters, ViT usually suffers from serious overfitting problems with a relatively limited number of training samples. In addition, ViT generally demands heavy computing resources, which limit its deployment on resource-constrained devices. As a type of model-compression method,model binarization is potentially a good choice to solve the above problems. Compared with the full-precision one, the model with the binarization method replaces complex tensor multiplication with simple bit-wise binary operations and represents full-precision model parameters and activations with only 1-bit ones, which potentially solves the problem of model size and computational complexity, respectively. In this paper, we find that the decline of the accuracy of the binary ViT model is mainly due to the information loss of the Attention module and the Value vector. Therefore, we propose a novel model binarization technique, called Group Superposition Binarization (GSB), to deal with these issues. Furthermore, in order to further improve the performance of the binarization model, we have investigated the gradient calculation procedure in the binarization process and derived more proper gradient calculation equations for GSB to reduce the influence of gradient mismatch. Then, the knowledge distillation technique is introduced to alleviate the performance degradation caused by model binarization. Experiments on three datasets with limited numbers of training samples demonstrate that the proposed GSB model achieves state-of-the-art performance among the binary quantization schemes and exceeds its full-precision counterpart on some indicators.

MaskRange: A Mask-classification Model for Range-view based LiDAR Segmentation

Range-view based LiDAR segmentation methods are attractive for practical applications due to their direct inheritance from efficient 2D CNN architectures. In literature, most range-view based methods follow the per-pixel classification paradigm. Recently, in the image segmentation domain, another paradigm formulates segmentation as a mask-classification problem and has achieved remarkable performance. This raises an interesting question: can the mask-classification paradigm benefit the range-view based LiDAR segmentation and achieve better performance than the counterpart per-pixel paradigm? To answer this question, we propose a unified mask-classification model, MaskRange, for the range-view based LiDAR semantic and panoptic segmentation. Along with the new paradigm, we also propose a novel data augmentation method to deal with overfitting, context-reliance, and class-imbalance problems. Extensive experiments are conducted on the SemanticKITTI benchmark. Among all published range-view based methods, our MaskRange achieves state-of-the-art performance with $66.10$ mIoU on semantic segmentation and promising results with $53.10$ PQ on panoptic segmentation with high efficiency. Our code will be released.

Semantics-Guided Moving Object Segmentation with 3D LiDAR

Moving object segmentation (MOS) is a task to distinguish moving objects, e.g., moving vehicles and pedestrians, from the surrounding static environment. The segmentation accuracy of MOS can have an influence on odometry, map construction, and planning tasks. In this paper, we propose a semantics-guided convolutional neural network for moving object segmentation. The network takes sequential LiDAR range images as inputs. Instead of segmenting the moving objects directly, the network conducts single-scan-based semantic segmentation and multiple-scan-based moving object segmentation in turn. The semantic segmentation module provides semantic priors for the MOS module, where we propose an adjacent scan association (ASA) module to convert the semantic features of adjacent scans into the same coordinate system to fully exploit the cross-scan semantic features. Finally, by analyzing the difference between the transformed features, reliable MOS result can be obtained quickly. Experimental results on the SemanticKITTI MOS dataset proves the effectiveness of our work.

Unsupervised Visible-light Images Guided Cross-Spectrum Depth Estimation from Dual-Modality Cameras

Cross-spectrum depth estimation aims to provide a depth map in all illumination conditions with a pair of dual-spectrum images. It is valuable for autonomous vehicle applications when the vehicle is equipped with two cameras of different modalities. However, images captured by different-modality cameras can be photometrically quite different. Therefore, cross-spectrum depth estimation is a very challenging problem. Moreover, the shortage of large-scale open-source datasets also retards further research in this field. In this paper, we propose an unsupervised visible-light image guided cross-spectrum (i.e., thermal and visible-light, TIR-VIS in short) depth estimation framework given a pair of RGB and thermal images captured from a visible-light camera and a thermal one. We first adopt a base depth estimation network using RGB-image pairs. Then we propose a multi-scale feature transfer network to transfer features from the TIR-VIS domain to the VIS domain at the feature level to fit the trained depth estimation network. At last, we propose a cross-spectrum depth cycle consistency to improve the depth result of dual-spectrum image pairs. Meanwhile, we release a large dual-spectrum depth estimation dataset with visible-light and far-infrared stereo images captured in different scenes to the society. The experiment result shows that our method achieves better performance than the compared existing methods. Our datasets is available at https://github.com/whitecrow1027/VIS-TIR-Datasets.