Low-Light Enhancement (LLE) is aimed at improving the quality of
photos/videos captured under low-light conditions. It is worth noting that most
existing LLE methods do not take advantage of geometric modeling. We believe
that incorporating geometric information can enhance LLE performance, as it
provides insights into the physical structure of the scene that influences
illumination conditions. To address this, we propose a Geometry-Guided
Low-Light Enhancement Refine Framework (GG-LLERF) designed to assist low-light
enhancement models in learning improved features for LLE by integrating
geometric priors into the feature representation space. In this paper, we
employ depth priors as the geometric representation. Our approach focuses on
the integration of depth priors into various LLE frameworks using a unified
methodology. This methodology comprises two key novel modules. First, a
depth-aware feature extraction module is designed to inject depth priors into
the image representation. Then, Hierarchical Depth-Guided Feature Fusion Module
(HDGFFM) is formulated with a cross-domain attention mechanism, which combines
depth-aware features with the original image features within the LLE model. We
conducted extensive experiments on public low-light image and video enhancement
benchmarks. The results illustrate that our designed framework significantly
enhances existing LLE methods.

使用几何建模的深度图先验指导低光增强，提供了改进的低光特征学习，并在各种低光增强框架中应用。

几何感知低光图像和视频增强方法

Geometric-Aware Low-Light Image and Video Enhancement via Depth Guidance

Category-level object pose estimation aims to predict the 6D pose as well as
the 3D metric size of arbitrary objects from a known set of categories. Recent
methods harness shape prior adaptation to map the observed point cloud into the
canonical space and apply Umeyama algorithm to recover the pose and size.
However, their shape prior integration strategy boosts pose estimation
indirectly, which leads to insufficient pose-sensitive feature extraction and
slow inference speed. To tackle this problem, in this paper, we propose a novel
geometry-guided Residual Object Bounding Box Projection network RBP-Pose that
jointly predicts object pose and residual vectors describing the displacements
from the shape-prior-indicated object surface projections on the bounding box
towards the real surface projections. Such definition of residual vectors is
inherently zero-mean and relatively small, and explicitly encapsulates spatial
cues of the 3D object for robust and accurate pose regression. We enforce
geometry-aware consistency terms to align the predicted pose and residual
vectors to further boost performance.

本文提出了一种新颖的几何引导残差边框投影网络 RBP-Pose，它在预测物体姿态和残差向量方面具有较好的性能。