5,113 research outputs found
Model-based encoding parameter optimization for 3D point cloud compression
Rate-distortion optimal 3D point cloud compression
is very challenging due to the irregular structure of 3D point
clouds. For a popular 3D point cloud codec that uses octrees for
geometry compression and JPEG for color compression, we first
find analytical models that describe the relationship between the
encoding parameters and the bitrate and distortion, respectively.
We then use our models to formulate the rate-distortion optimization
problem as a constrained convex optimization problem
and apply an interior point method to solve it. Experimental
results for six 3D point clouds show that our technique gives
similar results to exhaustive search at only about 1.57% of its
computational cost
Learning quadrangulated patches for 3D shape parameterization and completion
We propose a novel 3D shape parameterization by surface patches, that are
oriented by 3D mesh quadrangulation of the shape. By encoding 3D surface detail
on local patches, we learn a patch dictionary that identifies principal surface
features of the shape. Unlike previous methods, we are able to encode surface
patches of variable size as determined by the user. We propose novel methods
for dictionary learning and patch reconstruction based on the query of a noisy
input patch with holes. We evaluate the patch dictionary towards various
applications in 3D shape inpainting, denoising and compression. Our method is
able to predict missing vertices and inpaint moderately sized holes. We
demonstrate a complete pipeline for reconstructing the 3D mesh from the patch
encoding. We validate our shape parameterization and reconstruction methods on
both synthetic shapes and real world scans. We show that our patch dictionary
performs successful shape completion of complicated surface textures.Comment: To be presented at International Conference on 3D Vision 2017, 201
Deep Generative Modeling of LiDAR Data
Building models capable of generating structured output is a key challenge
for AI and robotics. While generative models have been explored on many types
of data, little work has been done on synthesizing lidar scans, which play a
key role in robot mapping and localization. In this work, we show that one can
adapt deep generative models for this task by unravelling lidar scans into a 2D
point map. Our approach can generate high quality samples, while simultaneously
learning a meaningful latent representation of the data. We demonstrate
significant improvements against state-of-the-art point cloud generation
methods. Furthermore, we propose a novel data representation that augments the
2D signal with absolute positional information. We show that this helps
robustness to noisy and imputed input; the learned model can recover the
underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201
Rate-Distortion Modeling for Bit Rate Constrained Point Cloud Compression
As being one of the main representation formats of 3D real world and
well-suited for virtual reality and augmented reality applications, point
clouds have gained a lot of popularity. In order to reduce the huge amount of
data, a considerable amount of research on point cloud compression has been
done. However, given a target bit rate, how to properly choose the color and
geometry quantization parameters for compressing point clouds is still an open
issue. In this paper, we propose a rate-distortion model based quantization
parameter selection scheme for bit rate constrained point cloud compression.
Firstly, to overcome the measurement uncertainty in evaluating the distortion
of the point clouds, we propose a unified model to combine the geometry
distortion and color distortion. In this model, we take into account the
correlation between geometry and color variables of point clouds and derive a
dimensionless quantity to represent the overall quality degradation. Then, we
derive the relationships of overall distortion and bit rate with the
quantization parameters. Finally, we formulate the bit rate constrained point
cloud compression as a constrained minimization problem using the derived
polynomial models and deduce the solution via an iterative numerical method.
Experimental results show that the proposed algorithm can achieve optimal
decoded point cloud quality at various target bit rates, and substantially
outperform the video-rate-distortion model based point cloud compression
scheme.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video
Technolog
Lightweight super resolution network for point cloud geometry compression
This paper presents an approach for compressing point cloud geometry by
leveraging a lightweight super-resolution network. The proposed method involves
decomposing a point cloud into a base point cloud and the interpolation
patterns for reconstructing the original point cloud. While the base point
cloud can be efficiently compressed using any lossless codec, such as
Geometry-based Point Cloud Compression, a distinct strategy is employed for
handling the interpolation patterns. Rather than directly compressing the
interpolation patterns, a lightweight super-resolution network is utilized to
learn this information through overfitting. Subsequently, the network parameter
is transmitted to assist in point cloud reconstruction at the decoder side.
Notably, our approach differentiates itself from lookup table-based methods,
allowing us to obtain more accurate interpolation patterns by accessing a
broader range of neighboring voxels at an acceptable computational cost.
Experiments on MPEG Cat1 (Solid) and Cat2 datasets demonstrate the remarkable
compression performance achieved by our method.Comment: 10 pages, 3 figures, 2 tables, and 27 reference
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