In recent years, intelligent vehicles operating in urban environments have demonstrated the capability to autonomously execute various tasks, such as object detection, lane detection, segmentation, etc. This advancement is facilitated by the extensive datasets accumulated by researchers, alongside advancements in intelligent algorithms, as well as significant breakthroughs in software and hardware. However, within the autonomous driving community, there is a scarcity of data regarding scenarios encountered in mining environments. This scarcity presents challenges and bottlenecks for the advancement of comprehensive autonomous driving systems and autonomousoperations. Although we previously released our dataset, AutoMine, which includes over 18 hours of driving data in open-pit mines, its scope is limited to two specific tasks. This scope limitation impedes the training and validation of the majority of algorithms for different tasks in this particular scenario. To broaden the scope of autonomous driving visual tasks in mining environments, we have curated a diverse collection encompassing multiple tasks, including detection, segmentation, tracking, etc. Additionally, we have established benchmarks and set up baselines for the aforementioned multiple tasks. By comparing the performance differences of visual algorithms between mining areas and other scenarios, we demonstrate the distinctive characteristics of mining regions in an intuitive manner. We have developed a suite of tools for converting annotated data into the standardized format used in existing driving datasets. Our aspiration is to establish data and benchmark foundations, supporting research endeavors in intelligent transportation within mining environments and autonomous driving in comprehensive scenarios. Our project website can be seen in AutoMine, and the dataset can be downloaded via AutoMine-Benchmark.

Novel view synthesis is a crucial task in the field of visualization and computer graphics. Although current methods, such as neural radiance fields (NeRF), have proven versatile for novel view synthesis, they often assume that input views are uniformly distributed in space. This means that each view captures a portion of the real-world scene, with each area being equally represented in the input views. In contrast, real-world scenarios involve randomly captured images with an uneven spatial distribution of input views, where some views of the scene are observed more frequently than others. This discrepancy poses significant challenges for current methods, underscoring the need to address the issue of nonuniformity in practical applications. To tackle this challenge, we propose a new dataset and an effective approach. Our extensive dataset comprises diverse scenes, capturing images in various real-world environments. Unlike previous novel view synthesis datasets, which have uniform or near-uniform input view distributions, our dataset effectively reflects the nonuniform distribution of images in real-world settings. Additionally, we introduce a simple yet effective method that significantly enhances the performance of current techniques under nonuniform conditions without modifying their pipelines. We anticipate that our data and method will serve as a valuable source of inspiration for future research in this area.

Recent methods, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have demonstrated remarkable capabilities in novel view synthesis. However, despite their success in producing high-quality images for viewpoints similar to those seen during training, they struggle when generating detailed images from viewpoints that significantly deviate from the training set, particularly in close-up views. The primary challenge stems from the lack of specific training data for close-up views, leading to the inability of current methods to render these views accurately. To address this issue, we introduce a novel pseudo-label-based learning strategy. This approach leverages pseudo-labels derived from existing training data to provide targeted supervision across a wide range of close-up viewpoints. Recognizing the absence of benchmarks for this specific challenge, we also present a new dataset designed to assess the effectiveness of both current and future methods in this area. Our extensive experiments demonstrate the efficacy of our approach.

Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is violated in dynamic object regions and occlusions. Consequently, existing methods show poor accuracy in dynamic scenes, and the estimated depth map is blurred at object boundaries because they are usually occluded in other training views. In this paper, we propose SC-DepthV3 for addressing the challenges. Specifically, we introduce an external pretrained monocular depth estimation model for generating single-image depth prior, namely pseudo-depth, based on which we propose novel losses to boost self-supervised training. As a result, our model can predict sharp and accurate depth maps, even when training from monocular videos of highly dynamic scenes. We demonstrate the significantly superior performance of our method over previous methods on six challenging datasets, and we provide detailed ablation studies for the proposed terms.

Road detection is a critically important task for self-driving cars. By employing LiDAR data, recent works have significantly improved the accuracy of road detection. However, relying on LiDAR sensors limits the application of those methods when only cameras are available. In this paper, we propose a novel road detection approach with RGB images being the only input. Specifically, we exploit pseudo-LiDAR using depth estimation and propose a feature fusion network in which RGB images and learned depth information are fused for improved road detection. To optimize the network architecture and improve the efficiency of our network, we propose a method to search for the information propagation paths. Finally, to reduce the computational cost, we design a modality distillation strategy to avoid using depth estimation networks during inference. The resulting model eliminates the reliance on LiDAR sensors and achieves state-of-the-art performance on two challenging benchmarks, KITTI and R2D.

Monocular visual odometry (VO) is an important task in robotics and computer vision. Thus far, how to build accurate and robust monocular VO systems that can work well in diverse scenarios remains largely unsolved. In this article, we propose a framework to exploit monocular depth estimation for improving VO. The core of our framework is a monocular depth estimation module with a strong generalization capability for diverse scenes. It consists of two separate working modes to assist the localization and mapping. With a single monocular image input, the depth estimation module predicts a relative depth to help the localization module on improving the accuracy. With a sparse depth map and an RGB image input, the depth estimation module can generate accurate scale-consistent depth for dense mapping. Compared with current learning-based VO methods, our method demonstrates a stronger generalization ability to diverse scenes. More significantly, our framework is able to boost the performances of existing geometry-based VO methods by a large margin.

Visual SLAM in low illumination scenes remains a considerably challenging task since the available amount of appearance information frequently stays insufficient. To tackle with this problem, we propose a novel SLAM framework by using both appearance information and thermal information, which possesses illumination-free recognizable contents, in a flexible manner. The key idea is to continuously update a RGB-T map, which contains both RGB and thermal map points to implement location and mapping. More specifically, in our SLAM system, we detect features in both RGB and thermal images and combine them together to update the RGB-T map and implement simultaneous location and mapping. Both quantitative and qualitative results demonstrate the effectiveness of our framework, especially under low illumination environments.