Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network

Mandikal, Priyanka; Babu, R. Venkatesh

doi:10.48550/arxiv.1901.08906

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jin et al [ 28 ] extended [ 27 ] using weak supervision to generate depth images for improved reconstruction. Mandikal et al [ 29 ] predicted a low-resolution point cloud from a 2D image and upsampled it to reconstruct a high-resolution point cloud. DeformNet [ 30 ] retrieved a point cloud shape template and fused it with image features.…”

Section: Related Workmentioning

confidence: 99%

“…However, they faced limitations in generating high-resolution point clouds, as achieving higher resolution necessitates increasing the decoder’s output neuron count. While Mandikal et al’s [ 29 ] method can generate higher-resolution point clouds, it initially reconstructs sparse point clouds and incrementally increases resolution, potentially losing object details similar to upscaling a compressed 2D image. 3D-PSRNet’s [ 45 ] reconstruction method incorporates object parts into network training, allowing the segmentation network to capture detailed object part features.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Part2Point: A Part-Oriented Point Cloud Reconstruction Framework

Feng,

Zeng,

Liang

2023

Sensors

View full text Add to dashboard Cite

Three-dimensional object modeling is necessary for developing virtual and augmented reality applications. Traditionally, application engineers must manually use art software to edit object shapes or exploit LIDAR to scan physical objects for constructing 3D models. This is very time-consuming and costly work. Fortunately, GPU recently provided a cost-effective solution for massive data computation. With GPU support, many studies have proposed 3D model generators based on different learning architectures, which can automatically convert 2D object pictures into 3D object models with good performance. However, as the demand for model resolution increases, the required computing time and memory space increase as significantly as the parameters of the learning architecture, which seriously degrades the efficiency of 3D model construction and the feasibility of resolution improvement. To resolve this problem, this paper proposes a part-oriented point cloud reconstruction framework called Part2Point. This framework segments the object’s parts, reconstructs the point cloud for individual object parts, and combines the part point clouds into the complete object point cloud. Therefore, it can reduce the number of learning network parameters at the exact resolution, effectively minimizing the calculation time cost and the required memory space. Moreover, it can improve the resolution of the reconstructed point cloud so that the reconstructed model can present more details of object parts.

show abstract