Surface Normal Clustering for Implicit Representation of Manhattan Scenes

• URL: http://arxiv.org/abs/2212.01331v4
• Date: Wed, 27 Sep 2023 15:39:49 GMT
• Title: Surface Normal Clustering for Implicit Representation of Manhattan Scenes
• Authors: Nikola Popovic, Danda Pani Paudel, Luc Van Gool
• Abstract summary: view synthesis and 3D modeling using implicit neural field representation are shown to be very effective for multi-view cameras. Most existing methods that exploit additional supervision require dense pixel-wise labels or localized scene priors. In this work, we aim to leverage the geometric prior of Manhattan scenes to improve the implicit neural radiance field representations.
• Score: 67.16489078998961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Novel view synthesis and 3D modeling using implicit neural field representation are shown to be very effective for calibrated multi-view cameras. Such representations are known to benefit from additional geometric and semantic supervision. Most existing methods that exploit additional supervision require dense pixel-wise labels or localized scene priors. These methods cannot benefit from high-level vague scene priors provided in terms of scenes' descriptions. In this work, we aim to leverage the geometric prior of Manhattan scenes to improve the implicit neural radiance field representations. More precisely, we assume that only the knowledge of the indoor scene (under investigation) being Manhattan is known -- with no additional information whatsoever -- with an unknown Manhattan coordinate frame. Such high-level prior is used to self-supervise the surface normals derived explicitly in the implicit neural fields. Our modeling allows us to cluster the derived normals and exploit their orthogonality constraints for self-supervision. Our exhaustive experiments on datasets of diverse indoor scenes demonstrate the significant benefit of the proposed method over the established baselines. The source code is available at https://github.com/nikola3794/normal-clustering-nerf.

Related papers

• Self-supervised Learning of Neural Implicit Feature Fields for Camera Pose Refinement [32.335953514942474]
  This paper proposes to jointly learn the scene representation along with a 3D dense feature field and a 2D feature extractor. We learn the underlying geometry of the scene with an implicit field through volumetric rendering and design our feature field to leverage intermediate geometric information encoded in the implicit field. Visual localization is then achieved by aligning the image-based features and the rendered volumetric features.
  arXiv  Detail & Related papers  (2024-06-12T17:51:53Z)
• N-BVH: Neural ray queries with bounding volume hierarchies [51.430495562430565]
  In 3D computer graphics, the bulk of a scene's memory usage is due to polygons and textures. We devise N-BVH, a neural compression architecture designed to answer arbitrary ray queries in 3D. Our method provides faithful approximations of visibility, depth, and appearance attributes.
  arXiv  Detail & Related papers  (2024-05-25T13:54:34Z)
• NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function [55.86697795177619]
  Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. We introduce a new paradigm for learning neural gradient functions, which encourages the neural network to fit the input point clouds. Our excellent results on widely used benchmarks demonstrate that our method can learn more accurate normals for both unoriented and oriented normal estimation tasks.
  arXiv  Detail & Related papers  (2023-11-01T09:25:29Z)
• Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement [50.56517624931987]
  The reconstruction of indoor scenes from multi-view RGB images is challenging due to the coexistence of flat and texture-less regions. Recent methods leverage neural radiance fields aided by predicted surface normal priors to recover the scene geometry. This work aims to reconstruct high-fidelity surfaces with fine-grained details by addressing the above limitations.
  arXiv  Detail & Related papers  (2023-09-14T12:05:29Z)
• Tetra-NeRF: Representing Neural Radiance Fields Using Tetrahedra [31.654710376807593]
  This paper proposes to use an adaptive representation based on tetrahedra obtained by Delaunay instead of uniform subdivision or point-based representations. We show that such a representation enables efficient training and leads to state-of-the-art results.
  arXiv  Detail & Related papers  (2023-04-19T21:44:27Z)
• NPC: Neural Point Characters from Video [21.470471345454524]
  High-fidelity human 3D models can now be learned directly from videos. Previous methods avoid using a template but rely on a costly or ill-posed mapping from observation to canonical space. We propose a hybrid point-based representation for reconstructing animatable characters.
  arXiv  Detail & Related papers  (2023-04-04T17:59:22Z)
• HQ3DAvatar: High Quality Controllable 3D Head Avatar [65.70885416855782]
  This paper presents a novel approach to building highly photorealistic digital head avatars. Our method learns a canonical space via an implicit function parameterized by a neural network. At test time, our method is driven by a monocular RGB video.
  arXiv  Detail & Related papers  (2023-03-25T13:56:33Z)
• Coordinates Are NOT Lonely -- Codebook Prior Helps Implicit Neural 3D Representations [29.756718435405983]
  Implicit neural 3D representation has achieved impressive results in surface or scene reconstruction and novel view synthesis. Existing approaches, such as Neural Radiance Field (NeRF) and its variants, usually require dense input views. We introduce a novel coordinate-based model, CoCo-INR, for implicit neural 3D representation.
  arXiv  Detail & Related papers  (2022-10-20T11:13:50Z)
• Neural 3D Scene Reconstruction with the Manhattan-world Assumption [58.90559966227361]
  This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Planar constraints can be conveniently integrated into the recent implicit neural representation-based reconstruction methods. The proposed method outperforms previous methods by a large margin on 3D reconstruction quality.
  arXiv  Detail & Related papers  (2022-05-05T17:59:55Z)
• Semantic Implicit Neural Scene Representations With Semi-Supervised Training [47.61092265963234]
  We show that implicit neural scene representations can be leveraged to perform per-point semantic segmentation. Our method is simple, general, and only requires a few tens of labeled 2D segmentation masks. We explore two novel applications for this semantically aware implicit neural scene representation.
  arXiv  Detail & Related papers  (2020-03-28T00:43:17Z)

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.