3D visual grounding aims to find the object within point clouds mentioned by
free-form natural language descriptions with rich semantic cues. However,
existing methods either extract the sentence-level features coupling all words
or focus more on object names, which would lose the word-level information or
neglect other attributes. To alleviate these issues, we present EDA that
Explicitly Decouples the textual attributes in a sentence and conducts Dense
Alignment between such fine-grained language and point cloud objects.
Specifically, we first propose a text decoupling module to produce textual
features for every semantic component. Then, we design two losses to supervise
the dense matching between two modalities: position alignment loss and semantic
alignment loss. On top of that, we further introduce a new visual grounding
task, locating objects without object names, which can thoroughly evaluate the
model's dense alignment capacity. Through experiments, we achieve
state-of-the-art performance on two widely-adopted 3D visual grounding
datasets, ScanRefer and SR3D/NR3D, and obtain absolute leadership on our
newly-proposed task. The source code is available at
this https URL

本研究提出 EDA 方法，通过文本解耦和密集对齐技术实现了 3D 点云中丰富语义描述的物体检索，并在已有数据集上实现了最优结果，并在新提出的任务上取得了绝对领先。

EDA: 三维视觉引导的显式文本解耦和密集对齐

EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual  Grounding

This paper considers the generic problem of dense alignment between two
images, whether they be two frames of a video, two widely different views of a
scene, two paintings depicting similar content, etc. Whereas each such task is
typically addressed with a domain-specific solution, we show that a simple
unsupervised approach performs surprisingly well across a range of tasks. Our
main insight is that parametric and non-parametric alignment methods have
complementary strengths. We propose a two-stage process: first, a feature-based
parametric coarse alignment using one or more homographies, followed by
non-parametric fine pixel-wise alignment. Coarse alignment is performed using
RANSAC on off-the-shelf deep features. Fine alignment is learned in an
unsupervised way by a deep network which optimizes a standard structural
similarity metric (SSIM) between the two images, plus cycle-consistency.
Despite its simplicity, our method shows competitive results on a range of
tasks and datasets, including unsupervised optical flow on KITTI, dense
correspondences on Hpatches, two-view geometry estimation on YFCC100M,
localization on Aachen Day-Night, and, for the first time, fine alignment of
artworks on the Brughel dataset. Our code and data are available at
this http URL

本文提出了一种简单的无监督方法来完成不同任务下两张图像之间的密集对齐问题，采用参数化和非参数化的两个阶段方法来实现初步和精细对齐，其中初步对齐使用了 RANSAC 和现成的深度特征，精细对齐使用了一种优化标准结构相似度度量的深度网络，并且已被应用于多个领域的数据集，包括 KITTI、Hpatches、YFCC100M、Aachen Day-Night 和 Brughel dataset。

RANSAC-Flow：通用的两阶段图像对齐

RANSAC-Flow: generic two-stage image alignment

We propose a straightforward method that simultaneously reconstructs the 3D
facial structure and provides dense alignment. To achieve this, we design a 2D
representation called UV position map which records the 3D shape of a complete
face in UV space, then train a simple Convolutional Neural Network to regress
it from a single 2D image. We also integrate a weight mask into the loss
function during training to improve the performance of the network. Our method
does not rely on any prior face model, and can reconstruct full facial geometry
along with semantic meaning. Meanwhile, our network is very light-weighted and
spends only 9.8ms to process an image, which is extremely faster than previous
works. Experiments on multiple challenging datasets show that our method
surpasses other state-of-the-art methods on both reconstruction and alignment
tasks by a large margin.

本文提出一种简单的方法，可以同时重建三维人脸结构并提供密集对齐。该方法使用称为 UV 位置映射的二维表示来记录完整面部的三维形状，并使用简单卷积神经网络从单个二维图像中回归它。该方法不依赖于任何先前的人脸模型，并且可以重建全面部几何信息。与之前的作品相比，网络非常轻便，并且只需 9.8 毫秒即可处理图像。多次挑战数据集的实验表明，该方法在重建和对齐任务上均优于其他最先进的方法。