Single-image 3D human reconstruction aims to reconstruct the 3D textured
surface of the human body given a single image. While implicit function-based
methods recently achieved reasonable reconstruction performance, they still
bear limitations showing degraded quality in both surface geometry and texture
from an unobserved view. In response, to generate a realistic textured surface,
we propose ReFu, a coarse-to-fine approach that refines the projected backside
view image and fuses the refined image to predict the final human body. To
suppress the diffused occupancy that causes noise in projection images and
reconstructed meshes, we propose to train occupancy probability by
simultaneously utilizing 2D and 3D supervisions with occupancy-based volume
rendering. We also introduce a refinement architecture that generates
detail-preserving backside-view images with front-to-back warping. Extensive
experiments demonstrate that our method achieves state-of-the-art performance
in 3D human reconstruction from a single image, showing enhanced geometry and
texture quality from an unobserved view.

使用 ReFu 方法，结合 2D 和 3D 监督，利用减少扩散占用概率的体积渲染，生成具有细节保留的背面图像，并将经过修整的图像与前端的反向变形融合以预测出人体模型，最终在单张图像 3D 人体重建中取得了最新成就。

ReFu: 通过细节保留的单张照片三维人体建模，对未观察的视角进行优化和融合

ReFu: Refine and Fuse the Unobserved View for Detail-Preserving  Single-Image 3D Human Reconstruction

Single-image piece-wise planar 3D reconstruction aims to simultaneously
segment plane instances and recover 3D plane parameters from an image. Most
recent approaches leverage convolutional neural networks (CNNs) and achieve
promising results. However, these methods are limited to detecting a fixed
number of planes with certain learned order. To tackle this problem, we propose
a novel two-stage method based on associative embedding, inspired by its recent
success in instance segmentation. In the first stage, we train a CNN to map
each pixel to an embedding space where pixels from the same plane instance have
similar embeddings. Then, the plane instances are obtained by grouping the
embedding vectors in planar regions via an efficient mean shift clustering
algorithm. In the second stage, we estimate the parameter for each plane
instance by considering both pixel-level and instance-level consistencies. With
the proposed method, we are able to detect an arbitrary number of planes.
Extensive experiments on public datasets validate the effectiveness and
efficiency of our method. Furthermore, our method runs at 30 fps at the testing
time, thus could facilitate many real-time applications such as visual SLAM and
human-robot interaction. Code is available at
this https URL

本文提出了一种基于联想嵌入的两阶段方法，旨在从单个图像中同时分割平面实例并恢复三维平面参数，并且可以检测任意数量的平面，并且该方法可实现 30 fps 的运行速度，因此适用于实时应用，例如视觉 SLAM 和人机交互。

基于关联嵌入的单张图像分块平面三维重建

Single-Image Piece-wise Planar 3D Reconstruction via Associative  Embedding

Crowd counting on static images is a challenging problem due to scale
variations. Recently deep neural networks have been shown to be effective in
this task. However, existing neural-networks-based methods often use the
multi-column or multi-network model to extract the scale-relevant features,
which is more complicated for optimization and computation wasting. To this
end, we propose a novel multi-scale convolutional neural network (MSCNN) for
single image crowd counting. Based on the multi-scale blobs, the network is
able to generate scale-relevant features for higher crowd counting performances
in a single-column architecture, which is both accuracy and cost effective for
practical applications. Complemental results show that our method outperforms
the state-of-the-art methods on both accuracy and robustness with far less
number of parameters.

提出一种新型多尺度卷积神经网络（MSCNN）用于单张图像的人群计数，基于多尺度块，该网络能够生成与规模有关的特征以提高计数精度和鲁棒性，同时使用单列架构，更为精准和成本有效。实验证明，该方法的参数数量远低于现有的基于多列或多网络模型的方法，并且在计数精度和鲁棒性方面都优于现有的最先进方法。