Supervised learning methods to infer (hypothesize) depth of a scene from a
single image require costly per-pixel ground-truth. We follow a geometric
approach that exploits abundant stereo imagery to learn a model to hypothesize
scene structure without direct supervision. Although we train a network with
stereo pairs, we only require a single image at test time to hypothesize
disparity or depth. We propose a novel objective function that exploits the
bilateral cyclic relationship between the left and right disparities and we
introduce an adaptive regularization scheme that allows the network to handle
both the co-visible and occluded regions in a stereo pair. This process
ultimately produces a model to generate hypotheses for the 3-dimensional
structure of the scene as viewed in a single image. When used to generate a
single (most probable) estimate of depth, our method outperforms
state-of-the-art unsupervised monocular depth prediction methods on the KITTI
benchmarks. We show that our method generalizes well by applying our models
trained on KITTI to the Make3d dataset.

使用几何方法和立体图像训练卷积神经网络可以在单张图片中预测场景深度，性能优于最先进的无监督单目深度预测方法，并适用于不同数据集。

无监督单目深度预测的双向循环约束和自适应正则化

Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised  Monocular Depth Prediction

We propose a 3D object detection method for autonomous driving by fully
exploiting the sparse and dense, semantic and geometry information in stereo
imagery. Our method, called Stereo R-CNN, extends Faster R-CNN for stereo
inputs to simultaneously detect and associate object in left and right images.
We add extra branches after stereo Region Proposal Network (RPN) to predict
sparse keypoints, viewpoints, and object dimensions, which are combined with 2D
left-right boxes to calculate a coarse 3D object bounding box. We then recover
the accurate 3D bounding box by a region-based photometric alignment using left
and right RoIs. Our method does not require depth input and 3D position
supervision, however, outperforms all existing fully supervised image-based
methods. Experiments on the challenging KITTI dataset show that our method
outperforms the state-of-the-art stereo-based method by around 30% AP on both
3D detection and 3D localization tasks. Code has been released at
this https URL

提出了一种基于稀疏和稠密、语义和几何信息的 3D 目标检测方法，名为 Stereo R-CNN，在自动驾驶领域表现优异，无需 3D 位置监督，与现有的基于图像的全监督方法相比，性能更好，实验表明在 KITTI 数据集上，其在 3D 检测和 3D 定位任务中超过现有的基于立体视觉的方法约 30% 的 AP。

基于 Stereo R-CNN 的自动驾驶三维物体检测

Stereo R-CNN based 3D Object Detection for Autonomous Driving

The goal of this paper is to perform 3D object detection in the context of
autonomous driving. Our method first aims at generating a set of high-quality
3D object proposals by exploiting stereo imagery. We formulate the problem as
minimizing an energy function that encodes object size priors, placement of
objects on the ground plane as well as several depth informed features that
reason about free space, point cloud densities and distance to the ground. We
then exploit a CNN on top of these proposals to perform object detection. In
particular, we employ a convolutional neural net (CNN) that exploits context
and depth information to jointly regress to 3D bounding box coordinates and
object pose. Our experiments show significant performance gains over existing
RGB and RGB-D object proposal methods on the challenging KITTI benchmark. When
combined with the CNN, our approach outperforms all existing results in object
detection and orientation estimation tasks for all three KITTI object classes.
Furthermore, we experiment also with the setting where LIDAR information is
available, and show that using both LIDAR and stereo leads to the best result.

本文利用立体图像对 3D 目标进行检测，通过能量函数优化，使用 CNN 进行目标检测和姿态估计，实现在 KITTI 测试中的最佳表现，并应用 LIDAR 信息进一步优化检测结果。

使用立体图像生成三维物体建议，实现准确的物体分类检测

3D Object Proposals using Stereo Imagery for Accurate Object Class  Detection

We propose a continuous optimization method for solving dense 3D scene flow
problems from stereo imagery. As in recent work, we represent the dynamic 3D
scene as a collection of rigidly moving planar segments. The scene flow problem
then becomes the joint estimation of pixel-to-segment assignment, 3D position,
normal vector and rigid motion parameters for each segment, leading to a
complex and expensive discrete-continuous optimization problem. In contrast, we
propose a purely continuous formulation which can be solved more efficiently.
Using a fine superpixel segmentation that is fixed a-priori, we propose a
factor graph formulation that decomposes the problem into photometric,
geometric, and smoothing constraints. We initialize the solution with a novel,
high-quality initialization method, then independently refine the geometry and
motion of the scene, and finally perform a global non-linear refinement using
Levenberg-Marquardt. We evaluate our method in the challenging KITTI Scene Flow
benchmark, ranking in third position, while being 3 to 30 times faster than the
top competitors.

本文提出了一种用于从立体影像中求解密集的三维场景流问题的连续优化方法，其中将动态三维场景表示为一系列刚性运动的平面段，使用预先确定的精细超像素分割，将问题分解为光度学、几何和平滑约束，并使用 Levenberg-Marquardt 进行全局非线性优化。