INPC: Implicit Neural Point Clouds for Radiance Field Rendering

• URL: http://arxiv.org/abs/2403.16862v1
• Date: Mon, 25 Mar 2024 15:26:32 GMT
• Title: INPC: Implicit Neural Point Clouds for Radiance Field Rendering
• Authors: Florian Hahlbohm, Linus Franke, Moritz Kappel, Susana Castillo, Marc Stamminger, Marcus Magnor,
• Abstract summary: We introduce a new approach for reconstruction and novel-view synthesis of real-world scenes. We propose a hybrid scene representation, which implicitly encodes a point cloud in a continuous octree-based probability field and a multi-resolution hash grid. Our method achieves fast inference at interactive frame rates, and can extract explicit point clouds to further enhance performance.
• Score: 5.64500060725726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a new approach for reconstruction and novel-view synthesis of unbounded real-world scenes. In contrast to previous methods using either volumetric fields, grid-based models, or discrete point cloud proxies, we propose a hybrid scene representation, which implicitly encodes a point cloud in a continuous octree-based probability field and a multi-resolution hash grid. In doing so, we combine the benefits of both worlds by retaining favorable behavior during optimization: Our novel implicit point cloud representation and differentiable bilinear rasterizer enable fast rendering while preserving fine geometric detail without depending on initial priors like structure-from-motion point clouds. Our method achieves state-of-the-art image quality on several common benchmark datasets. Furthermore, we achieve fast inference at interactive frame rates, and can extract explicit point clouds to further enhance performance.

Related papers

• Bridging Domain Gap of Point Cloud Representations via Self-Supervised Geometric Augmentation [15.881442863961531]
  We introduce a novel scheme for induced geometric invariance of point cloud representations across domains. On one hand, a novel pretext task of predicting translation of distances of augmented samples is proposed to alleviate centroid shift of point clouds. On the other hand, we pioneer an integration of the relational self-supervised learning on geometrically-augmented point clouds.
  arXiv  Detail & Related papers  (2024-09-11T02:39:19Z)
• D-NPC: Dynamic Neural Point Clouds for Non-Rigid View Synthesis from Monocular Video [53.83936023443193]
  This paper contributes to the field by introducing a new synthesis method for dynamic novel view from monocular video, such as smartphone captures. Our approach represents the as a $textitdynamic neural point cloud$, an implicit time-conditioned point cloud that encodes local geometry and appearance in separate hash-encoded neural feature grids.
  arXiv  Detail & Related papers  (2024-06-14T14:35:44Z)
• Point Cloud Compression with Implicit Neural Representations: A Unified Framework [54.119415852585306]
  We present a pioneering point cloud compression framework capable of handling both geometry and attribute components. Our framework utilizes two coordinate-based neural networks to implicitly represent a voxelized point cloud. Our method exhibits high universality when contrasted with existing learning-based techniques.
  arXiv  Detail & Related papers  (2024-05-19T09:19:40Z)
• Multiway Point Cloud Mosaicking with Diffusion and Global Optimization [74.3802812773891]
  We introduce a novel framework for multiway point cloud mosaicking (named Wednesday) At the core of our approach is ODIN, a learned pairwise registration algorithm that identifies overlaps and refines attention scores. Tested on four diverse, large-scale datasets, our method state-of-the-art pairwise and rotation registration results by a large margin on all benchmarks.
  arXiv  Detail & Related papers  (2024-03-30T17:29:13Z)
• Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach [83.05340155068721]
  We devise a new 3d point cloud generation framework using a divide-and-conquer approach. All patch generators are based on learnable priors, which aim to capture the information of geometry primitives. Experimental results on a variety of object categories from the most popular point cloud dataset, ShapeNet, show the effectiveness of the proposed patch-wise point cloud generation.
  arXiv  Detail & Related papers  (2023-07-22T11:10:39Z)
• Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids [84.90863397388776]
  We propose to directly use signed distance function (SDF) in sparse voxel block grids for fast and accurate scene reconstruction without distances. Our globally sparse and locally dense data structure exploits surfaces' spatial sparsity, enables cache-friendly queries, and allows direct extensions to multi-modal data. Experiments show that our approach is 10x faster in training and 100x faster in rendering while achieving comparable accuracy to state-of-the-art neural implicit methods.
  arXiv  Detail & Related papers  (2023-05-22T16:50:19Z)
• Pointersect: Neural Rendering with Cloud-Ray Intersection [30.485621062087585]
  We propose a novel method that renders point clouds as if they are surfaces. The proposed method is differentiable and requires no scene-specific optimization.
  arXiv  Detail & Related papers  (2023-04-24T18:36:49Z)
• GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian Manifolds [2.8811433060309763]
  We propose a novel, one-shot point cloud simplification method. It preserves both the salient structural features and the overall shape of a point cloud without any prior surface reconstruction step. We evaluate our method on several benchmark and self-acquired point clouds, compare it to a range of existing methods, demonstrate its application in downstream tasks of registration and surface reconstruction.
  arXiv  Detail & Related papers  (2023-03-27T14:05:34Z)
• Differentiable Point-Based Radiance Fields for Efficient View Synthesis [57.56579501055479]
  We propose a differentiable rendering algorithm for efficient novel view synthesis. Our method is up to 300x faster than NeRF in both training and inference. For dynamic scenes, our method trains two orders of magnitude faster than STNeRF and renders at near interactive rate.
  arXiv  Detail & Related papers  (2022-05-28T04:36:13Z)
• Point Cloud Compression with Sibling Context and Surface Priors [47.96018990521301]
  We present a novel octree-based multi-level framework for large-scale point cloud compression. In this framework, we propose a new entropy model that explores the hierarchical dependency in an octree. We locally fit surfaces with a voxel-based geometry-aware module to provide geometric priors in entropy encoding.
  arXiv  Detail & Related papers  (2022-05-02T09:13:26Z)

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.