Due to the inherent ill-posed nature of 2D-3D projection, monocular 3D object
detection lacks accurate depth recovery ability. Although the deep neural
network (DNN) enables monocular depth-sensing from high-level learned features,
the pixel-level cues are usually omitted due to the deep convolution mechanism.
To benefit from both the powerful feature representation in DNN and pixel-level
geometric constraints, we reformulate the monocular object depth estimation as
a progressive refinement problem and propose a joint semantic and geometric
cost volume to model the depth error. Specifically, we first leverage neural
networks to learn the object position, dimension, and dense normalized 3D
object coordinates. Based on the object depth, the dense coordinates patch
together with the corresponding object features is reprojected to the image
space to build a cost volume in a joint semantic and geometric error manner.
The final depth is obtained by feeding the cost volume to a refinement network,
where the distribution of semantic and geometric error is regularized by direct
depth supervision. Through effectively mitigating depth error by the refinement
framework, we achieve state-of-the-art results on both the KITTI and Waymo
datasets.

通过将单目物体深度估计重新制定为渐进细化问题，并提出一个联合的语义和几何代价体来建模深度误差，通过细化框架有效地缓解深度误差，在 KITTI 和 Waymo 数据集上实现了最先进的结果。

MonoJSG: 单目三维物体检测的联合语义和几何代价体

MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D  Object Detection

Large-scale language models (LMs) pretrained on massive corpora of text, such
as GPT-2, are powerful open-domain text generators. However, as our systematic
examination reveals, it is still challenging for such models to generate
coherent long passages of text (e.g., 1000 tokens), especially when the models
are fine-tuned to the target domain on a small corpus. Previous
planning-then-generation methods also fall short of producing such long text in
various domains. To overcome the limitations, we propose a simple but effective
method of generating text in a progressive manner, inspired by generating
images from low to high resolution. Our method first produces domain-specific
content keywords and then progressively refines them into complete passages in
multiple stages. The simple design allows our approach to take advantage of
pretrained LMs at each stage and effectively adapt to any target domain given
only a small set of examples. We conduct a comprehensive empirical study with a
broad set of evaluation metrics, and show that our approach significantly
improves upon the fine-tuned large LMs and various planning-then-generation
methods in terms of quality and sample efficiency. Human evaluation also
validates that our model generations are more coherent.

该论文提出了一种通过逐步完善领域特定的关键词，并在多个阶段逐步把它们转换为完整段落生成文本的简单而有效的方法，该方法利用了预先训练的大规模语言模型，在质量和样本效率方面显着优于仅仅在小语料库上微调的大型语言模型和其他规划 - 生成方法。