Despite the advancements in deep learning for camera relocalization tasks,
obtaining ground truth pose labels required for the training process remains a
costly endeavor. While current weakly supervised methods excel in lightweight
label generation, their performance notably declines in scenarios with sparse
views. In response to this challenge, we introduce WSCLoc, a system capable of
being customized to various deep learning-based relocalization models to
enhance their performance under weakly-supervised and sparse view conditions.
This is realized with two stages. In the initial stage, WSCLoc employs a
multilayer perceptron-based structure called WFT-NeRF to co-optimize image
reconstruction quality and initial pose information. To ensure a stable
learning process, we incorporate temporal information as input. Furthermore,
instead of optimizing SE(3), we opt for $\mathfrak{sim}(3)$ optimization to
explicitly enforce a scale constraint. In the second stage, we co-optimize the
pre-trained WFT-NeRF and WFT-Pose. This optimization is enhanced by
Time-Encoding based Random View Synthesis and supervised by inter-frame
geometric constraints that consider pose, depth, and RGB information. We
validate our approaches on two publicly available datasets, one outdoor and one
indoor. Our experimental results demonstrate that our weakly-supervised
relocalization solutions achieve superior pose estimation accuracy in
sparse-view scenarios, comparable to state-of-the-art camera relocalization
methods. We will make our code publicly available.

WSCLoc 系统用于弱监督和稀疏视图条件下的摄像机定位任务，通过两个阶段的联合优化提高了深度学习模型的性能，实现了超过当前方法的姿态估计准确性。

WSCLoc: 弱监督稀疏视图相机定位

WSCLoc: Weakly-Supervised Sparse-View Camera Relocalization

Neural radiance fields (NeRFs) have gained popularity across various
applications. However, they face challenges in the sparse view setting, lacking
sufficient constraints from volume rendering. Reconstructing and understanding
a 3D scene from sparse and unconstrained cameras is a long-standing problem in
classical computer vision with diverse applications. While recent works have
explored NeRFs in sparse, unconstrained view scenarios, their focus has been
primarily on enhancing reconstruction and novel view synthesis. Our approach
takes a broader perspective by posing the question: "from where has each point
been seen?" -- which gates how well we can understand and reconstruct it. In
other words, we aim to determine the origin or provenance of each 3D point and
its associated information under sparse, unconstrained views. We introduce
ProvNeRF, a model that enriches a traditional NeRF representation by
incorporating per-point provenance, modeling likely source locations for each
point. We achieve this by extending implicit maximum likelihood estimation
(IMLE) for stochastic processes. Notably, our method is compatible with any
pre-trained NeRF model and the associated training camera poses. We demonstrate
that modeling per-point provenance offers several advantages, including
uncertainty estimation, criteria-based view selection, and improved novel view
synthesis, compared to state-of-the-art methods.

ProvNeRF 模型通过将每个 3D 点的来源或起源和相关信息建模为稀疏、不受约束视图下的随机过程的概率最大化估计的方式，丰富了传统的 NeRF 表示。与现有方法相比，该模型具有不确定性估计、基于准则的视图选择和提高新视图合成的优势。