We propose a new framework that formulates point cloud registration as a
denoising diffusion process from noisy transformation to object transformation.
During training stage, object transformation diffuses from ground-truth
transformation to random distribution, and the model learns to reverse this
noising process. In sampling stage, the model refines randomly generated
transformation to the output result in a progressive way. We derive the
variational bound in closed form for training and provide implementations of
the model. Our work provides the following crucial findings: (i) In contrast to
most existing methods, our framework, Diffusion Probabilistic Models for Point
Cloud Registration (PCRDiffusion) does not require repeatedly update source
point cloud to refine the predicted transformation. (ii) Point cloud
registration, one of the representative discriminative tasks, can be solved by
a generative way and the unified probabilistic formulation. Finally, we discuss
and provide an outlook on the application of diffusion model in different
scenarios for point cloud registration. Experimental results demonstrate that
our model achieves competitive performance in point cloud registration. In
correspondence-free and correspondence-based scenarios, PCRDifussion can both
achieve exceeding 50\% performance improvements.

我们提出了一个新的框架，将点云配准问题看作是从噪声变换到对象变换的去噪扩散过程，并通过生成模型和统一概率表达解决了代表性的判别任务之一 - 点云配准问题。

PCRDiffusion：基于扩散的点云配准概率模型

PCRDiffusion: Diffusion Probabilistic Models for Point Cloud  Registration

Visual grounding (VG) tasks involve explicit cross-modal alignment, as
semantically corresponding image regions are to be located for the language
phrases provided. Existing approaches complete such visual-text reasoning in a
single-step manner. Their performance causes high demands on large-scale
anchors and over-designed multi-modal fusion modules based on human priors,
leading to complicated frameworks that may be difficult to train and overfit to
specific scenarios. Even worse, such once-for-all reasoning mechanisms are
incapable of refining boxes continuously to enhance query-region matching. In
contrast, in this paper, we formulate an iterative reasoning process by
denoising diffusion modeling. Specifically, we propose a language-guided
diffusion framework for visual grounding, LG-DVG, which trains the model to
progressively reason queried object boxes by denoising a set of noisy boxes
with the language guide. To achieve this, LG-DVG gradually perturbs
query-aligned ground truth boxes to noisy ones and reverses this process step
by step, conditional on query semantics. Extensive experiments for our proposed
framework on five widely used datasets validate the superior performance of
solving visual grounding, a cross-modal alignment task, in a generative way.
The source codes are available at
https://github.com/iQua/vgbase/tree/DiffusionVG.

通过去噪扩散建模的语言引导扩散框架（LG-DVG）提出了一种逐步推理的视觉定位方法，可持续改进查询 - 区域匹配，在跨模态对齐任务中以生成方式解决视觉定位，并在多个数据集上验证其超凡性能。