PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting

• URL: http://arxiv.org/abs/2412.12096v2
• Date: Sun, 23 Mar 2025 19:46:58 GMT
• Title: PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting
• Abstract summary: We present PanSplat, a generalizable, feed-forward approach that efficiently supports resolution up to 4K (2048 $times$ 4096)<n>Our approach features a tailored spherical 3D Gaussian pyramid with a Fibonacci lattice arrangement, enhancing image quality while reducing information redundancy.<n>Experiments demonstrate that PanSplat achieves superior efficiency and image quality across both synthetic and real-world datasets.
• Score: 38.47270795377126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advent of portable 360{\deg} cameras, panorama has gained significant attention in applications like virtual reality (VR), virtual tours, robotics, and autonomous driving. As a result, wide-baseline panorama view synthesis has emerged as a vital task, where high resolution, fast inference, and memory efficiency are essential. Nevertheless, existing methods are typically constrained to lower resolutions (512 $\times$ 1024) due to demanding memory and computational requirements. In this paper, we present PanSplat, a generalizable, feed-forward approach that efficiently supports resolution up to 4K (2048 $\times$ 4096). Our approach features a tailored spherical 3D Gaussian pyramid with a Fibonacci lattice arrangement, enhancing image quality while reducing information redundancy. To accommodate the demands of high resolution, we propose a pipeline that integrates a hierarchical spherical cost volume and Gaussian heads with local operations, enabling two-step deferred backpropagation for memory-efficient training on a single A100 GPU. Experiments demonstrate that PanSplat achieves state-of-the-art results with superior efficiency and image quality across both synthetic and real-world datasets. Code is available at https://github.com/chengzhag/PanSplat.

Related papers

• Quantile Rendering: Efficiently Embedding High-dimensional Feature on 3D Gaussian Splatting [52.18697134979677]
  Recent advancements in computer vision have successfully extended Open-vocabulary segmentation (OVS) to the 3D domain by leveraging 3D Gaussian Splatting (3D-GS)<n>Existing methods employ codebooks or feature compression, causing information loss, thereby degrading segmentation quality.<n>We introduce Quantile Rendering (Q-Render), a novel rendering strategy for 3D Gaussians that efficiently handles high-dimensional features while maintaining high fidelity.<n>Our framework outperforms state-of-the-art methods, while enabling real-time rendering with an approximate 43.7x speedup on 512-D feature maps.
  arXiv  Detail & Related papers  (2025-12-24T04:16:18Z)
• Virtual Memory for 3D Gaussian Splatting [1.278093617645299]
  Gaussian Splatting represents a breakthrough in the field of novel view rendering.<n>Recent advances have increased the size of Splatting scenes that can be created.
  arXiv  Detail & Related papers  (2025-06-24T08:31:33Z)
• Splatter-360: Generalizable 360$^{\circ}$ Gaussian Splatting for Wide-baseline Panoramic Images [52.48351378615057]
  textitSplatter-360 is a novel end-to-end generalizable 3DGS framework to handle wide-baseline panoramic images.<n>We introduce a 3D-aware bi-projection encoder to mitigate the distortions inherent in panoramic images.<n>This enables robust 3D-aware feature representations and real-time rendering capabilities.
  arXiv  Detail & Related papers  (2024-12-09T06:58:31Z)
• VR-Splatting: Foveated Radiance Field Rendering via 3D Gaussian Splatting and Neural Points [4.962171160815189]
  We propose a novel hybrid approach that combines the strengths of both point rendering directions regarding performance sweet spots. For the fovea only, we use neural points with a convolutional neural network for the small pixel footprint, which provides sharp, detailed output. Our evaluation confirms that our approach increases sharpness and details compared to a standard VR-ready 3DGS configuration.
  arXiv  Detail & Related papers  (2024-10-23T14:54:48Z)
• MEGA: Memory-Efficient 4D Gaussian Splatting for Dynamic Scenes [49.36091070642661]
  This paper introduces a memory-efficient framework for 4DGS. It achieves a storage reduction by approximately 190$times$ and 125$times$ on the Technicolor and Neural 3D Video datasets. It maintains comparable rendering speeds and scene representation quality, setting a new standard in the field.
  arXiv  Detail & Related papers  (2024-10-17T14:47:08Z)
• HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting [48.59338619051709]
  HiFi4G is an explicit and compact Gaussian-based approach for high-fidelity human performance rendering from dense footage. It achieves a substantial compression rate of approximately 25 times, with less than 2MB of storage per frame.
  arXiv  Detail & Related papers  (2023-12-06T12:36:53Z)
• LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS [55.85673901231235]
  We introduce LightGaussian, a method for transforming 3D Gaussians into a more compact format. Inspired by Network Pruning, LightGaussian identifies Gaussians with minimal global significance on scene reconstruction. LightGaussian achieves an average 15x compression rate while boosting FPS from 144 to 237 within the 3D-GS framework.
  arXiv  Detail & Related papers  (2023-11-28T21:39:20Z)
• Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis [0.552480439325792]
  High-fidelity scene reconstruction with an optimized 3D Gaussian splat representation has been introduced for novel view synthesis from sparse image sets. We propose a compressed 3D Gaussian splat representation that utilizes sensitivity-aware vector clustering with quantization-aware training to compress directional colors and Gaussian parameters.
  arXiv  Detail & Related papers  (2023-11-17T14:40:43Z)
• 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering [103.32717396287751]
  We propose 4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes. A neuralvoxel encoding algorithm inspired by HexPlane is proposed to efficiently build features from 4D neural voxels. Our 4D-GS method achieves real-time rendering under high resolutions, 82 FPS at an 800$times$800 resolution on an 3090 GPU.
  arXiv  Detail & Related papers  (2023-10-12T17:21:41Z)
• CoordFill: Efficient High-Resolution Image Inpainting via Parameterized Coordinate Querying [52.91778151771145]
  In this paper, we try to break the limitations for the first time thanks to the recent development of continuous implicit representation. Experiments show that the proposed method achieves real-time performance on the 2048$times$2048 images using a single GTX 2080 Ti GPU.
  arXiv  Detail & Related papers  (2023-03-15T11:13:51Z)
• CUF: Continuous Upsampling Filters [25.584630142930123]
  In this paper, we consider one of the most important operations in image processing: upsampling. We propose to parameterize upsampling kernels as neural fields. This parameterization leads to a compact architecture that obtains a 40-fold reduction in the number of parameters when compared with competing arbitrary-scale super-resolution architectures.
  arXiv  Detail & Related papers  (2022-10-13T12:45:51Z)

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.