Oblique-MERF: Revisiting and Improving MERF for Oblique Photography

• URL: http://arxiv.org/abs/2404.09531v1
• Date: Mon, 15 Apr 2024 07:51:29 GMT
• Title: Oblique-MERF: Revisiting and Improving MERF for Oblique Photography
• Authors: Xiaoyi Zeng, Kaiwen Song, Leyuan Yang, Bailin Deng, Juyong Zhang,
• Abstract summary: We introduce an innovative adaptive occupancy plane optimized during the volume rendering process and a smoothness regularization term for view-dependent color to address these issues. Our approach, termed oblique-MERF, surpasses state-of-the-art real-time methods by approximately 0.7 dB, reduces VRAM usage by about 40%, and achieves higher rendering frame rates with more realistic rendering outcomes across most viewpoints.
• Score: 32.864777068264665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural implicit fields have established a new paradigm for scene representation, with subsequent work achieving high-quality real-time rendering. However, reconstructing 3D scenes from oblique aerial photography presents unique challenges, such as varying spatial scale distributions and a constrained range of tilt angles, often resulting in high memory consumption and reduced rendering quality at extrapolated viewpoints. In this paper, we enhance MERF to accommodate these data characteristics by introducing an innovative adaptive occupancy plane optimized during the volume rendering process and a smoothness regularization term for view-dependent color to address these issues. Our approach, termed Oblique-MERF, surpasses state-of-the-art real-time methods by approximately 0.7 dB, reduces VRAM usage by about 40%, and achieves higher rendering frame rates with more realistic rendering outcomes across most viewpoints.

Related papers

• ZipIR: Latent Pyramid Diffusion Transformer for High-Resolution Image Restoration [75.0053551643052]
  We introduce ZipIR, a novel framework that enhances efficiency, scalability, and long-range modeling for high-res image restoration. ZipIR employs a highly compressed latent representation that compresses image 32x, effectively reducing the number of spatial tokens. ZipIR surpasses existing diffusion-based methods, offering unmatched speed and quality in restoring high-resolution images from severely degraded inputs.
  arXiv  Detail & Related papers  (2025-04-11T14:49:52Z)
• 3D Gaussian Splatting with Normal Information for Mesh Extraction and Improved Rendering [8.59572577251833]
  We propose a novel regularization method using the gradients of a signed distance function estimated from the Gaussians. We demonstrate the effectiveness of our approach on datasets such as Mip-NeRF360, Tanks and Temples, and Deep-Blending.
  arXiv  Detail & Related papers  (2025-01-14T18:40:33Z)
• VRVVC: Variable-Rate NeRF-Based Volumetric Video Compression [59.14355576912495]
  NeRF-based video has revolutionized visual media by delivering photorealistic Free-Viewpoint Video (FVV) experiences. The substantial data volumes pose significant challenges for storage and transmission. We propose VRVVC, a novel end-to-end joint variable-rate framework for video compression.
  arXiv  Detail & Related papers  (2024-12-16T01:28:04Z)
• MVSBoost: An Efficient Point Cloud-based 3D Reconstruction [4.282795945742752]
  Efficient and accurate 3D reconstruction is crucial for various applications, including augmented and virtual reality, medical imaging, and cinematic special effects. Traditional Multi-View Stereo (MVS) systems have been fundamental in these applications, but implicit 3D scene modeling has introduced new possibilities for handling complex topologies and continuous surfaces.
  arXiv  Detail & Related papers  (2024-06-19T13:02:17Z)
• PASTA: Towards Flexible and Efficient HDR Imaging Via Progressively Aggregated Spatio-Temporal Alignment [91.38256332633544]
  PASTA is a Progressively Aggregated Spatio-Temporal Alignment framework for HDR deghosting. Our approach achieves effectiveness and efficiency by harnessing hierarchical representation during feature distanglement. Experimental results showcase PASTA's superiority over current SOTA methods in both visual quality and performance metrics.
  arXiv  Detail & Related papers  (2024-03-15T15:05:29Z)
• VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction [59.40711222096875]
  We present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets.
  arXiv  Detail & Related papers  (2024-02-27T11:40:50Z)
• MixRT: Mixed Neural Representations For Real-Time NeRF Rendering [24.040636076067393]
  We propose MixRT, a novel NeRF representation that includes a low-quality mesh, a view-dependent displacement map, and a compressed NeRF model. This design effectively harnesses the capabilities of existing graphics hardware, thus enabling real-time NeRF rendering on edge devices.
  arXiv  Detail & Related papers  (2023-12-19T04:14:11Z)
• HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces [71.1071688018433]
  Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to render. We propose a method, HybridNeRF, that leverages the strengths of both representations by rendering most objects as surfaces. We improve error rates by 15-30% while achieving real-time framerates (at least 36 FPS) for virtual-reality resolutions (2Kx2K)
  arXiv  Detail & Related papers  (2023-12-05T22:04:49Z)
• Multi-tiling Neural Radiance Field (NeRF) -- Geometric Assessment on Large-scale Aerial Datasets [5.391764618878545]
  In this paper, we aim to scale the Neural Radiance Fields (NeRF) on large-scael aerial datasets. Specifically, we introduce a location-specific sampling technique as well as a multi-camera tiling (MCT) strategy to reduce memory consumption. We implement our method on a representative approach, Mip-NeRF, and compare its geometry performance with threephotgrammetric MVS pipelines.
  arXiv  Detail & Related papers  (2023-10-01T00:21:01Z)
• MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes [61.01853377661283]
  We present a Memory-Efficient Radiance Field representation that achieves real-time rendering of large-scale scenes in a browser. We introduce a novel contraction function that maps scene coordinates into a bounded volume while still allowing for efficient ray-box intersection.
  arXiv  Detail & Related papers  (2023-02-23T18:59:07Z)
• SteerNeRF: Accelerating NeRF Rendering via Smooth Viewpoint Trajectory [20.798605661240355]
  We propose a new way to speed up rendering using 2D neural networks. A low-resolution feature map is rendered first by volume rendering, then a lightweight 2D neural is applied to generate the image at target resolution. We show that the proposed method can achieve competitive rendering quality while reducing the rendering time with little memory overhead, enabling 30FPS at 1080P image resolution with a low memory footprint.
  arXiv  Detail & Related papers  (2022-12-15T00:02:36Z)
• CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations [77.90883737693325]
  This paper proposes CLONeR, which significantly improves upon NeRF by allowing it to model large outdoor driving scenes observed from sparse input sensor views. This is achieved by decoupling occupancy and color learning within the NeRF framework into separate Multi-Layer Perceptrons (MLPs) trained using LiDAR and camera data, respectively. In addition, this paper proposes a novel method to build differentiable 3D Occupancy Grid Maps (OGM) alongside the NeRF model, and leverage this occupancy grid for improved sampling of points along a ray for rendering in metric space.
  arXiv  Detail & Related papers  (2022-09-02T17:44:50Z)
• PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images [75.87721926918874]
  We present Progressively Deblurring Radiance Field (PDRF) PDRF is a novel approach to efficiently reconstruct high quality radiance fields from blurry images. We show that PDRF is 15X faster than previous State-of-The-Art scene reconstruction methods.
  arXiv  Detail & Related papers  (2022-08-17T03:42:29Z)

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.