PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes

• URL: http://arxiv.org/abs/2412.03451v1
• Date: Wed, 04 Dec 2024 16:38:07 GMT
• Title: PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes
• Authors: Bin Tan, Rui Yu, Yujun Shen, Nan Xue,
• Abstract summary: PlanarSplatting is an ultra-fast and accurate surface reconstruction approach for multiview indoor images. PlanarSplatting reconstructs an indoor scene in 3 minutes while having significantly better geometric accuracy.
• Score: 32.00236197233923
• License:
• Abstract: This paper presents PlanarSplatting, an ultra-fast and accurate surface reconstruction approach for multiview indoor images. We take the 3D planes as the main objective due to their compactness and structural expressiveness in indoor scenes, and develop an explicit optimization framework that learns to fit the expected surface of indoor scenes by splatting the 3D planes into 2.5D depth and normal maps. As our PlanarSplatting operates directly on the 3D plane primitives, it eliminates the dependencies on 2D/3D plane detection and plane matching and tracking for planar surface reconstruction. Furthermore, the essential merits of plane-based representation plus CUDA-based implementation of planar splatting functions, PlanarSplatting reconstructs an indoor scene in 3 minutes while having significantly better geometric accuracy. Thanks to our ultra-fast reconstruction speed, the largest quantitative evaluation on the ScanNet and ScanNet++ datasets over hundreds of scenes clearly demonstrated the advantages of our method. We believe that our accurate and ultrafast planar surface reconstruction method will be applied in the structured data curation for surface reconstruction in the future. The code of our CUDA implementation will be publicly available. Project page: https://icetttb.github.io/PlanarSplatting/

Related papers

• SliceOcc: Indoor 3D Semantic Occupancy Prediction with Vertical Slice Representation [50.420711084672966]
  We present SliceOcc, an RGB camera-based model specifically tailored for indoor 3D semantic occupancy prediction. Experimental results on the EmbodiedScan dataset demonstrate that SliceOcc achieves a mIoU of 15.45% across 81 indoor categories.
  arXiv  Detail & Related papers  (2025-01-28T03:41:24Z)
• MonoPlane: Exploiting Monocular Geometric Cues for Generalizable 3D Plane Reconstruction [37.481945507799594]
  This paper presents a generalizable 3D plane detection and reconstruction framework named MonoPlane. We first leverage large-scale pre-trained neural networks to obtain the depth and surface normals from a single image. These monocular geometric cues are then incorporated into a proximity-guided RANSAC framework to sequentially fit each plane instance.
  arXiv  Detail & Related papers  (2024-11-02T12:15:29Z)
• AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings [26.845588648999417]
  We tackle the problem of estimating the planar surfaces in a 3D scene from posed images. We propose a method that predicts multi-view consistent plane embeddings that complement geometry when clustering points into planes. We show through extensive evaluation on the ScanNetV2 dataset that our new method outperforms existing approaches.
  arXiv  Detail & Related papers  (2024-06-13T09:49:31Z)
• PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fields [34.99249208739048]
  PlanarNeRF is a novel framework capable of detecting dense 3D planes through online learning. It enhances 3D plane detection with concurrent appearance and geometry knowledge. A lightweight plane fitting module is proposed to estimate plane parameters.
  arXiv  Detail & Related papers  (2023-12-30T03:48:22Z)
• NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance [59.08521048003009]
  We propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces. The advantage of 3D Gaussian Splatting is that it can generate dense point clouds with detailed structure. We introduce a scale regularizer to pull the centers close to the surface by enforcing the 3D Gaussians to be extremely thin.
  arXiv  Detail & Related papers  (2023-12-01T07:04:47Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos [32.286637700503995]
  PlanarRecon is a framework for globally coherent detection and reconstruction of 3D planes from a posed monocular video. A learning-based tracking and fusion module is designed to merge planes from previous fragments to form a coherent global plane reconstruction. Experiments show that the proposed approach achieves state-of-the-art performances on the ScanNet dataset while being real-time.
  arXiv  Detail & Related papers  (2022-06-15T17:59:16Z)
• 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)
• LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering [59.63979143021241]
  We formulate the task of 360 layout estimation as a problem of predicting depth on the horizon line of a panorama. We propose the Differentiable Depth Rendering procedure to make the conversion from layout to depth prediction differentiable. Our method achieves state-of-the-art performance on numerous 360 layout benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-01T15:48:41Z)
• Dynamic Plane Convolutional Occupancy Networks [4.607145155913717]
  We propose Dynamic Plane Convolutional Occupancy Networks to push further the quality of 3D surface reconstruction. A fully-connected network learns to predict plane parameters that best describe the shapes of objects or scenes. Our method shows superior performance in surface reconstruction from unoriented point clouds in ShapeNet as well as an indoor scene dataset.
  arXiv  Detail & Related papers  (2020-11-11T14:24:52Z)
• KAPLAN: A 3D Point Descriptor for Shape Completion [80.15764700137383]
  KAPLAN is a 3D point descriptor that aggregates local shape information via a series of 2D convolutions. In each of those planes, point properties like normals or point-to-plane distances are aggregated into a 2D grid and abstracted into a feature representation with an efficient 2D convolutional encoder. Experiments on public datasets show that KAPLAN achieves state-of-the-art performance for 3D shape completion.
  arXiv  Detail & Related papers  (2020-07-31T21:56:08Z)

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.