We present a neural-field-based large-scale reconstruction system that fuses
lidar and vision data to generate high-quality reconstructions that are
geometrically accurate and capture photo-realistic textures. This system adapts
the state-of-the-art neural radiance field (NeRF) representation to also
incorporate lidar data which adds strong geometric constraints on the depth and
surface normals. We exploit the trajectory from a real-time lidar SLAM system
to bootstrap a Structure-from-Motion (SfM) procedure to both significantly
reduce the computation time and to provide metric scale which is crucial for
lidar depth loss. We use submapping to scale the system to large-scale
environments captured over long trajectories. We demonstrate the reconstruction
system with data from a multi-camera, lidar sensor suite onboard a legged
robot, hand-held while scanning building scenes for 600 metres, and onboard an
aerial robot surveying a multi-storey mock disaster site-building. Website:
this https URL

本研究提出了一种基于神经场的大规模重建系统，它利用激光雷达和视觉数据生成几何精确且具有照片级真实纹理的高质量重建，同时使用激光雷达 SLAM 系统提供深度和表面法线的强几何约束来改进传统的神经辐射场表示方法。

SiLVR: 大规模激光雷达视觉重建与神经辐射场用于机器人检测

SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields  for Robotic Inspection

Completing a corrupted image with correct structures and reasonable textures
for a mixed scene remains an elusive challenge. Since the missing hole in a
mixed scene of a corrupted image often contains various semantic information,
conventional two-stage approaches utilizing structural information often lead
to the problem of unreliable structural prediction and ambiguous image texture
generation. In this paper, we propose a Semantic Guidance and Evaluation
Network (SGE-Net) to iteratively update the structural priors and the inpainted
image in an interplay framework of semantics extraction and image inpainting.
It utilizes semantic segmentation map as guidance in each scale of inpainting,
under which location-dependent inferences are re-evaluated, and, accordingly,
poorly-inferred regions are refined in subsequent scales. Extensive experiments
on real-world images of mixed scenes demonstrated the superiority of our
proposed method over state-of-the-art approaches, in terms of clear boundaries
and photo-realistic textures.

本研究提出了一种基于语义引导和评估网络的图像修复算法，该算法在每个缩放级别利用语义分割图指导图像修复过程，并利用迭代更新方法实现结构先验和图像修复的协同更新，实验结果表明该方法在图像纹理清晰度和真实感等方面具有优越性。

语义感知的混合场景图像修复的指导与评估

Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed  Scenes

Despite the breakthroughs in accuracy and speed of single image
super-resolution using faster and deeper convolutional neural networks, one
central problem remains largely unsolved: how do we recover the finer texture
details when we super-resolve at large upscaling factors? The behavior of
optimization-based super-resolution methods is principally driven by the choice
of the objective function. Recent work has largely focused on minimizing the
mean squared reconstruction error. The resulting estimates have high peak
signal-to-noise ratios, but they are often lacking high-frequency details and
are perceptually unsatisfying in the sense that they fail to match the fidelity
expected at the higher resolution. In this paper, we present SRGAN, a
generative adversarial network (GAN) for image super-resolution (SR). To our
knowledge, it is the first framework capable of inferring photo-realistic
natural images for 4x upscaling factors. To achieve this, we propose a
perceptual loss function which consists of an adversarial loss and a content
loss. The adversarial loss pushes our solution to the natural image manifold
using a discriminator network that is trained to differentiate between the
super-resolved images and original photo-realistic images. In addition, we use
a content loss motivated by perceptual similarity instead of similarity in
pixel space. Our deep residual network is able to recover photo-realistic
textures from heavily downsampled images on public benchmarks. An extensive
mean-opinion-score (MOS) test shows hugely significant gains in perceptual
quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of
the original high-resolution images than to those obtained with any
state-of-the-art method.

本文提出了 SRGAN 框架，通过引入对抗性损失和感知损失函数，成功应用 GAN 进行图像超分辨率，能够生成 4 倍放大因子下的自然真实图像。大量实验和 MOS 测试证明 SRGAN 在视觉效果上具有显著的优势。