Point2Pix: Photo-Realistic Point Cloud Rendering via Neural Radiance Fields
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Abstract (arXiv)
Synthesizing photo-realistic images from a point cloud is challenging because of the sparsity of point cloud representation. Recent Neural Radiance Fields and extensions are proposed to synthesize realistic images from 2D input. In this paper, we present Point2Pix as a novel point renderer to link the 3D sparse point clouds with 2D dense image pixels. Taking advantage of the point cloud 3D prior and NeRF rendering pipeline, our method can synthesize high-quality images from colored point clouds, generally for novel indoor scenes. To improve the efficiency of ray sampling, we propose point-guided sampling, which focuses on valid samples. Also, we present Point Encoding to build Multi-scale Radiance Fields that provide discriminative 3D point features. Finally, we propose Fusion Encoding to efficiently synthesize high-quality images. Extensive experiments on the ScanNet and ArkitScenes datasets demonstrate the effectiveness and generalization.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 41 of its 59 references that are in the map. First posted on arXiv 29 Mar 2023.
Builds on
41 of 59 references.
References · 41
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29 more
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Built on by
Citing papers per year: 2024: 7; 2025: 4; 2026: 1.
12 papers.
- 2024LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenescited by 14
- 2025RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Imagescited by 12
- 2024X-Ray: A Sequential 3D Representation For Generationcited by 9
- 2024Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representationcited by 7
- 2024HashPoint: Accelerated Point Searching and Sampling for Neural Renderingcited by 4
- 2025Real-time Neural Rendering of LiDAR Point Cloudscited by 1
- 2025Sparse Point Cloud Patches Rendering via Splitting 2D Gaussianscited by 1
- 2024Methods and strategies for improving the novel view synthesis quality of neural radiation fieldcited by 0
- 2024GPN: Generative Point-based NeRFcited by 0
- 2024NARVis: Neural Accelerated Rendering for Real-Time Scientific Point Cloud Visualizationcited by 0
- 2025CRISTAL: Real-time Camera Registration in Static LiDAR Scans using Neural Renderingcited by 0
- 2026Bridging the Dimensionality Gap: A Taxonomy and Survey of 2D Vision Model Adaptation for 3D Analysiscited by 0
- 2026Bridging the Dimensionality Gap: A Taxonomy and Survey of 2D Vision Model Adaptation for 3D Analysis3 Apr 2026
- 2025CRISTAL: Real-time Camera Registration in Static LiDAR Scans using Neural Rendering20 Nov 2025
- 2025Sparse Point Cloud Patches Rendering via Splitting 2D Gaussians14 May 2025
- 2025RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images18 Mar 2025
- 2025Real-time Neural Rendering of LiDAR Point Clouds17 Feb 2025
- 2024NARVis: Neural Accelerated Rendering for Real-Time Scientific Point Cloud Visualization26 Jul 2024
- 2024LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes1 May 2024
- 2024X-Ray: A Sequential 3D Representation For Generation22 Apr 2024
- 2024HashPoint: Accelerated Point Searching and Sampling for Neural Rendering22 Apr 2024
- 2024GPN: Generative Point-based NeRF12 Apr 2024
- 2024Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representation25 Jan 2024
- 2024Methods and strategies for improving the novel view synthesis quality of neural radiation field23 Jan 2024