3D-R2N2: 单多视角3D对象重建的统一方法

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3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction 是一篇在三维重建领域具有影响力的论文，由Christopher B. Choy等人提出。该论文的核心创新在于引入了一种名为3D Recurrent Reconstruction Neural Network (3D-R2N2) 的新颖递归神经网络架构。3D-R2N2旨在通过利用形状先验知识，实现对单个和多视角对象的鲁棒三维重构。在3D-R2N2中，研究者们设计了一个网络，可以从大量的合成数据集中学习图像与对应3D形状之间的映射。这个网络的关键特性是其能够接受一个或多个来自不同视角的对象实例图像，然后输出一个以3D占用网格形式的物体重构结果。与先前的工作相比，3D-R2N2的一个显著优势在于它不需要任何图像注解或物体类别标签来进行训练或测试，这使得其在实际应用中更加灵活且减少了人工干预的需求。论文的重要贡献包括： 1. 单视图重构：实验结果显示，3D-R2N2在单视图重构任务上超越了当时的最先进的方法，这表明其在处理单个输入图像时也能准确地恢复出物体的三维结构，对于基于单一图像的3D重建技术来说，这是一个重大的进步。 2. 多视角重构：3D-R2N2不仅适用于单个视角，还能处理多视角的情况。传统的Structure from Motion (SfM) 和 Simultaneous Localization and Mapping (SLAM) 方法在某些复杂环境或动态场景下可能效果受限，而3D-R2N2则能够更好地应对这些挑战，从而实现了更全面、准确的物体在场景中的三维重建。总结来说，3D-R2N2论文为三维重建领域提供了一种统一且强大的框架，它革新了使用深度学习技术进行3D形状预测的方法，尤其在减少依赖于额外标注数据的情况下提高了性能。这对于推动三维重建技术的实际应用和研究有着重要意义。阅读这篇论文可以帮助读者深入了解如何利用深度学习解决单、多视角下的3D重建问题，并为未来在这个领域的发展提供新的思路和灵感。

4 C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese

2 Recurrent Neural Network

In this section we provide a brief overview of Long Short-Term Memory (LSTM)

networks and a variation of the LSTM called Gated Recurrent Units (GRU).

Long Short-Term Memory Unit. One of the most successful implemen-

tations of the hidden states of an RNN is the Long Short Term Memory (LSTM)

unit [33]. An LSTM unit explicitly controls the ﬂow from input to output, allow-

ing the network to overcome the vanishing gradient problem [33,38]. Speciﬁcally,

an LSTM unit consists of four components: memory units (a memory cell and

a hidden state), and three gates which control the ﬂow of information from the

input to the hidden state (input gate), from the hidden state to the output

(output gate), and from the previous hidden state to the current hidden state

(forget gate). More formally, at time step t when a new input x

is received, the

operation of an LSTM unit can be expressed as:

= σ(W

+ U

t−1

+ b

) (1)

= σ(W

+ U

t−1

+ b

) (2)

= σ(W

+ U

t−1

+ b

) (3)

= f

 s

t−1

+ i

 tanh(W

+ U

t−1

+ b

) (4)

= o

 tanh(s

) (5)

, f

, o

refer to the input gate, the output gate, and the forget gate, respec-

tively. s

and h

refer to the memory cell and the hidden state, respectively. We

use  to denote element-wise multiplication and the subscript t to refer to an

activation at time t. W

(·)

, U

(·)

are matrices that transform the current input x

and the previous hidden state h

t−1

, respectively, and b

(·)

represents the biases.

Gated Recurrent Unit. A variation of the LSTM unit is the Gated Re-

current Unit (GRU) proposed by Cho et al. [39]. An advantage of the GRU is

that there are fewer computations compared to the standard LSTM. In a GRU,

an update gate controls both the input and forget gates. Another diﬀerence is

that a reset gate is applied before the nonlinear transformation. More formally,

= σ(W

T x

+ U

∗ h

t−1

+ b

) (6)

= σ(W

T x

+ U

∗ h

t−1

+ b

) (7)

= (1 − u

)  h

t−1

+ u

 tanh(W

+ U

 h

t−1

) + b

) (8)

, r

, h

represent the update gate, the reset gate, and the hidden state re-

spectively. We follow the same notations as LSTM for matrices and biases.

3 3D Recurrent Reconstruction Neural Network

In this section, we introduce a novel architecture named the 3D Recurrent Re-

construction Network (3D-R2N2), which builds upon the standard LSTM and

GRU. The goal of the network is to perform both single- and multi-view 3D

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