In recent years, Neural Radiance Fields (NeRF) has revolutionized
three-dimensional (3D) reconstruction with its implicit representation.
Building upon NeRF, 3D Gaussian Splatting (3D-GS) has departed from the
implicit representation of neural networks and instead directly represents
scenes as point clouds with Gaussian-shaped distributions. While this shift has
notably elevated the rendering quality and speed of radiance fields but
inevitably led to a significant increase in memory usage. Additionally,
effectively rendering dynamic scenes in 3D-GS has emerged as a pressing
challenge. To address these concerns, this paper purposes a refined 3D Gaussian
representation for high-quality dynamic scene reconstruction. Firstly, we use a
deformable multi-layer perceptron (MLP) network to capture the dynamic offset
of Gaussian points and express the color features of points through hash
encoding and a tiny MLP to reduce storage requirements. Subsequently, we
introduce a learnable denoising mask coupled with denoising loss to eliminate
noise points from the scene, thereby further compressing 3D Gaussian model.
Finally, motion noise of points is mitigated through static constraints and
motion consistency constraints. Experimental results demonstrate that our
method surpasses existing approaches in rendering quality and speed, while
significantly reducing the memory usage associated with 3D-GS, making it highly
suitable for various tasks such as novel view synthesis, and dynamic mapping.

使用变形的多层感知器（MLP）网络捕捉动态偏移的高斯点并通过哈希编码和小型 MLP 来表示点的颜色特征，引入可学习的去噪掩模结合去噪损失以从场景中消除噪点，通过静态约束和运动一致性约束减轻点的运动噪声，实验证明我们的方法在渲染质量和速度上超过了现有方法，同时显著减少了与 3D-GS 相关的内存使用，非常适用于新的视角合成和动态建图等任务。

优化的三维高斯表示用于高质量动态场景重建

A Refined 3D Gaussian Representation for High-Quality Dynamic Scene  Reconstruction

We propose a novel method to reconstruct the 3D shapes of transparent objects
using hand-held captured images under natural light conditions. It combines the
advantage of explicit mesh and multi-layer perceptron (MLP) network, a hybrid
representation, to simplify the capture setting used in recent contributions.
After obtaining an initial shape through the multi-view silhouettes, we
introduce surface-based local MLPs to encode the vertex displacement field
(VDF) for the reconstruction of surface details. The design of local MLPs
allows to represent the VDF in a piece-wise manner using two layer MLP
networks, which is beneficial to the optimization algorithm. Defining local
MLPs on the surface instead of the volume also reduces the searching space.
Such a hybrid representation enables us to relax the ray-pixel correspondences
that represent the light path constraint to our designed ray-cell
correspondences, which significantly simplifies the implementation of
single-image based environment matting algorithm. We evaluate our
representation and reconstruction algorithm on several transparent objects with
ground truth models. Our experiments show that our method can produce
high-quality reconstruction results superior to state-of-the-art methods using
a simplified data acquisition setup.

本研究提出了一种基于手持拍摄的自然光下重建透明物体的 3D 形状的新方法，该方法采用了显式网格和多层感知器网络（MLP）的混合表示，通过多视图剪影获得初始形状，并使用基于表面的局部 MLP 编码顶点位移场（VDF）进行表面细节的重建。定义在表面而不是体积上的局部 MLPs 也减少了搜索空间，从而实现了基于单张图片的环境抠图算法的简化实现。经过多项实验证明，此方法可以在采集数据方面进行简化，并且可以确保重建质量优于现有的技术水平。

3D 透明物体混合 Mesh-neural 表示法的重建

Hybrid Mesh-neural Representation for 3D Transparent Object  Reconstruction

Point cloud analysis is challenging due to irregularity and unordered data
structure. To capture the 3D geometries, prior works mainly rely on exploring
sophisticated local geometric extractors using convolution, graph, or attention
mechanisms. These methods, however, incur unfavorable latency during inference,
and the performance saturates over the past few years. In this paper, we
present a novel perspective on this task. We notice that detailed local
geometrical information probably is not the key to point cloud analysis -- we
introduce a pure residual MLP network, called PointMLP, which integrates no
sophisticated local geometrical extractors but still performs very
competitively. Equipped with a proposed lightweight geometric affine module,
PointMLP delivers the new state-of-the-art on multiple datasets. On the
real-world ScanObjectNN dataset, our method even surpasses the prior best
method by 3.3% accuracy. We emphasize that PointMLP achieves this strong
performance without any sophisticated operations, hence leading to a superior
inference speed. Compared to most recent CurveNet, PointMLP trains 2x faster,
tests 7x faster, and is more accurate on ModelNet40 benchmark. We hope our
PointMLP may help the community towards a better understanding of point cloud
analysis. The code is available at this https URL.

本文提出了一种名为 PointMLP 的纯残差 MLP 网络，它不使用复杂的局部几何提取器，但仍能表现出很高的竞争力，装备了提出的轻量级几何仿射模块，PointMLP 在多个数据集上都达到了新的最佳状态，相对于大多数最近的 CurveNet，PointMLP 的训练速度快两倍，测试速度快七倍，并在 ModelNet40 基准测试中获得更高的精度。