GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2409.01581v1
• Date: Tue, 3 Sep 2024 03:35:04 GMT
• Title: GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting
• Authors: Zixuan Guo, Yifan Xie, Weijing Xie, Peng Huang, Fei Ma, Fei Richard Yu,
• Abstract summary: We propose a novel 2D-3D hybrid colored point cloud upsampling framework (GaussianPU) based on 3D Gaussian Splatting (3DGS) for robotic perception. A dual scale rendered image restoration network transforms sparse point cloud renderings into dense representations. We have made a series of enhancements to the vanilla 3DGS, enabling precise control over the number of points.
• Score: 11.60605616190011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dense colored point clouds enhance visual perception and are of significant value in various robotic applications. However, existing learning-based point cloud upsampling methods are constrained by computational resources and batch processing strategies, which often require subdividing point clouds into smaller patches, leading to distortions that degrade perceptual quality. To address this challenge, we propose a novel 2D-3D hybrid colored point cloud upsampling framework (GaussianPU) based on 3D Gaussian Splatting (3DGS) for robotic perception. This approach leverages 3DGS to bridge 3D point clouds with their 2D rendered images in robot vision systems. A dual scale rendered image restoration network transforms sparse point cloud renderings into dense representations, which are then input into 3DGS along with precise robot camera poses and interpolated sparse point clouds to reconstruct dense 3D point clouds. We have made a series of enhancements to the vanilla 3DGS, enabling precise control over the number of points and significantly boosting the quality of the upsampled point cloud for robotic scene understanding. Our framework supports processing entire point clouds on a single consumer-grade GPU, such as the NVIDIA GeForce RTX 3090, eliminating the need for segmentation and thus producing high-quality, dense colored point clouds with millions of points for robot navigation and manipulation tasks. Extensive experimental results on generating million-level point cloud data validate the effectiveness of our method, substantially improving the quality of colored point clouds and demonstrating significant potential for applications involving large-scale point clouds in autonomous robotics and human-robot interaction scenarios.

Related papers

• PointDreamer: Zero-shot 3D Textured Mesh Reconstruction from Colored Point Cloud by 2D Inpainting [18.44975150966385]
  Reconstructing textured meshes from colored point clouds is an important but challenging task in 3D graphics and vision. We propose PointDreamer, a novel framework for textured mesh reconstruction from colored point cloud. It produces meshes with enhanced fidelity and clarity by 2D image inpainting, taking advantage of the mature techniques and massive data of 2D vision.
  arXiv  Detail & Related papers  (2024-06-22T10:33:14Z)
• TriVol: Point Cloud Rendering via Triple Volumes [57.305748806545026]
  We present a dense while lightweight 3D representation, named TriVol, that can be combined with NeRF to render photo-realistic images from point clouds. Our framework has excellent generalization ability to render a category of scenes/objects without fine-tuning.
  arXiv  Detail & Related papers  (2023-03-29T06:34:12Z)
• Point2Pix: Photo-Realistic Point Cloud Rendering via Neural Radiance Fields [63.21420081888606]
  Recent Radiance Fields and extensions are proposed to synthesize realistic images from 2D input. We present Point2Pix as a novel point to link the 3D sparse point clouds with 2D dense image pixels.
  arXiv  Detail & Related papers  (2023-03-29T06:26:55Z)
• Ponder: Point Cloud Pre-training via Neural Rendering [93.34522605321514]
  We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural encoders. The learned point-cloud can be easily integrated into various downstream tasks, including not only high-level rendering tasks like 3D detection and segmentation, but low-level tasks like 3D reconstruction and image rendering.
  arXiv  Detail & Related papers  (2022-12-31T08:58:39Z)
• Leveraging Single-View Images for Unsupervised 3D Point Cloud Completion [53.93172686610741]
  Cross-PCC is an unsupervised point cloud completion method without requiring any 3D complete point clouds. To take advantage of the complementary information from 2D images, we use a single-view RGB image to extract 2D features. Our method even achieves comparable performance to some supervised methods.
  arXiv  Detail & Related papers  (2022-12-01T15:11:21Z)
• Points2NeRF: Generating Neural Radiance Fields from 3D point cloud [0.0]
  We propose representing 3D objects as Neural Radiance Fields (NeRFs) We leverage a hypernetwork paradigm and train the model to take a 3D point cloud with the associated color values. Our method provides efficient 3D object representation and offers several advantages over the existing approaches.
  arXiv  Detail & Related papers  (2022-06-02T20:23:33Z)
• SE-MD: A Single-encoder multiple-decoder deep network for point cloud generation from 2D images [2.4087148947930634]
  3D model generation from single 2D RGB images is a challenging and actively researched computer vision task. There are various issues like using inefficient 3D representation formats, weak 3D model generation backbones, inability to generate dense point clouds. A novel 2D RGB image to point cloud conversion technique is proposed, which improves the state of art in the field.
  arXiv  Detail & Related papers  (2021-06-17T10:48:46Z)
• ParaNet: Deep Regular Representation for 3D Point Clouds [62.81379889095186]
  ParaNet is a novel end-to-end deep learning framework for representing 3D point clouds. It converts an irregular 3D point cloud into a regular 2D color image, named point geometry image (PGI) In contrast to conventional regular representation modalities based on multi-view projection and voxelization, the proposed representation is differentiable and reversible.
  arXiv  Detail & Related papers  (2020-12-05T13:19:55Z)
• ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes [93.82668222075128]
  We propose a 3D detection architecture called ImVoteNet for RGB-D scenes. ImVoteNet is based on fusing 2D votes in images and 3D votes in point clouds. We validate our model on the challenging SUN RGB-D dataset.
  arXiv  Detail & Related papers  (2020-01-29T05:09:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.