The analysis of the multi-layer structure of wild forests is an important
challenge of automated large-scale forestry. While modern aerial LiDARs offer
geometric information across all vegetation layers, most datasets and methods
focus only on the segmentation and reconstruction of the top of canopy. We
release WildForest3D, which consists of 29 study plots and over 2000 individual
trees across 47 000m2 with dense 3D annotation, along with occupancy and height
maps for 3 vegetation layers: ground vegetation, understory, and overstory. We
propose a 3D deep network architecture predicting for the first time both 3D
point-wise labels and high-resolution layer occupancy rasters simultaneously.
This allows us to produce a precise estimation of the thickness of each
vegetation layer as well as the corresponding watertight meshes, therefore
meeting most forestry purposes. Both the dataset and the model are released in
open access: this https URL

利用多层次结构的 LiDAR 对野生森林进行 3D 注释，提出了一个同时预测 3D 点云标签和高分辨率层占用栅格的深度网络结构，以此实现对每层厚度和相应的闭合网格的精确估计，并发布了相关数据集和模型。

航空 LiDAR 扫描下稠密植被的多层建模

Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans

Human pose estimation from single images is a challenging problem that is
typically solved by supervised learning. Unfortunately, labeled training data
does not yet exist for many human activities since 3D annotation requires
dedicated motion capture systems. Therefore, we propose an unsupervised
approach that learns to predict a 3D human pose from a single image while only
being trained with 2D pose data, which can be crowd-sourced and is already
widely available. To this end, we estimate the 3D pose that is most likely over
random projections, with the likelihood estimated using normalizing flows on 2D
poses. While previous work requires strong priors on camera rotations in the
training data set, we learn the distribution of camera angles which
significantly improves the performance. Another part of our contribution is to
stabilize training with normalizing flows on high-dimensional 3D pose data by
first projecting the 2D poses to a linear subspace. We outperform the
state-of-the-art unsupervised human pose estimation methods on the benchmark
datasets Human3.6M and MPI-INF-3DHP in many metrics.

本文提出一种无监督的方法，通过学习从单张图像预测 3D 人体姿态来解决对于许多人类活动尚不存在标注训练数据的挑战，并且采用正则化流和对 2D 和 3D 姿态进行线性子空间投影来提高性能。

ElePose: 通过预测相机高度并在二维姿态上学习归一化流的无监督三维人体姿态估计

ElePose: Unsupervised 3D Human Pose Estimation by Predicting Camera  Elevation and Learning Normalizing Flows on 2D Poses

We propose a novel framework for fine-grained object recognition that learns
to recover object variation in 3D space from a single image, trained on an
image collection without using any ground-truth 3D annotation. We accomplish
this by representing an object as a composition of 3D shape and its appearance,
while eliminating the effect of camera viewpoint, in a canonical configuration.
Unlike conventional methods modeling spatial variation in 2D images only, our
method is capable of reconfiguring the appearance feature in a canonical 3D
space, thus enabling the subsequent object classifier to be invariant under 3D
geometric variation. Our representation also allows us to go beyond existing
methods, by incorporating 3D shape variation as an additional cue for object
recognition. To learn the model without ground-truth 3D annotation, we deploy a
differentiable renderer in an analysis-by-synthesis framework. By incorporating
3D shape and appearance jointly in a deep representation, our method learns the
discriminative representation of the object and achieves competitive
performance on fine-grained image recognition and vehicle re-identification. We
also demonstrate that the performance of 3D shape reconstruction is improved by
learning fine-grained shape deformation in a boosting manner.

该研究提出了一种新型的精细物体识别框架，能够从一个单一的图像中学习恢复物体在三维空间中的变化，并在无需使用任何基准 3D 注释情况下，在图像集合上进行训练。