We present a method for joint alignment of sparse in-the-wild image
collections of an object category. Most prior works assume either ground-truth
keypoint annotations or a large dataset of images of a single object category.
However, neither of the above assumptions hold true for the long-tail of the
objects present in the world. We present a self-supervised technique that
directly optimizes on a sparse collection of images of a particular
object/object category to obtain consistent dense correspondences across the
collection. We use pairwise nearest neighbors obtained from deep features of a
pre-trained vision transformer (ViT) model as noisy and sparse keypoint matches
and make them dense and accurate matches by optimizing a neural network that
jointly maps the image collection into a learned canonical grid. Experiments on
CUB and SPair-71k benchmarks demonstrate that our method can produce globally
consistent and higher quality correspondences across the image collection when
compared to existing self-supervised methods. Code and other material will be
made available at https://kampta.github.io/asic.

该研究提出了一种自监督技术的目标对象排列方法，使用神经网络将图像集映射到学习的规范网格，从而使得得到的密集一一对应点更加准确和稳定。

ASIC: 对野外稀疏图像集的对齐

ASIC: Aligning Sparse in-the-wild Image Collections

By moving a depth sensor around a room, we compute a 3D CAD model of the
environment, capturing the room shape and contents such as chairs, desks,
sofas, and tables. Rather than reconstructing geometry, we match, place, and
align each object in the scene to thousands of CAD models of objects. In
addition to the fully automatic system, the key technical contribution is a
novel approach for aligning CAD models to 3D scans, based on deep reinforcement
learning. This approach, which we call Learning-based ICP, outperforms prior
ICP methods in the literature, by learning the best points to match and
conditioning on object viewpoint. LICP learns to align using only synthetic
data and does not require ground truth annotation of object pose or keypoint
pair matching in real scene scans. While LICP is trained on synthetic data and
without 3D real scene annotations, it outperforms both learned local deep
feature matching and geometric based alignment methods in real scenes. The
proposed method is evaluated on real scenes datasets of SceneNN and ScanNet as
well as synthetic scenes of SUNCG. High quality results are demonstrated on a
range of real world scenes, with robustness to clutter, viewpoint, and
occlusion.

使用深度传感器在房间中捕获对象的 3D 模型，采用基于深度强化学习的学习式 ICP 方法对 CAD 模型进行对齐，优于先前文献中的 ICP 方法，能够在实际场景中实现高质量结果的对象对齐。