Surface Reconstruction from Point Clouds by Learning Predictive Context Priors

• URL: http://arxiv.org/abs/2204.11015v1
• Date: Sat, 23 Apr 2022 08:11:33 GMT
• Title: Surface Reconstruction from Point Clouds by Learning Predictive Context Priors
• Authors: Baorui Ma, Yu-Shen Liu, Matthias Zwicker, Zhizhong Han
• Abstract summary: Surface reconstruction from point clouds is vital for 3D computer vision. We introduce Predictive Context Priors by learning Predictive Queries for each specific point cloud at inference time. Our experimental results in surface reconstruction for single shapes or complex scenes show significant improvements over the state-of-the-art under widely used benchmarks.
• Score: 68.12457459590921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surface reconstruction from point clouds is vital for 3D computer vision. State-of-the-art methods leverage large datasets to first learn local context priors that are represented as neural network-based signed distance functions (SDFs) with some parameters encoding the local contexts. To reconstruct a surface at a specific query location at inference time, these methods then match the local reconstruction target by searching for the best match in the local prior space (by optimizing the parameters encoding the local context) at the given query location. However, this requires the local context prior to generalize to a wide variety of unseen target regions, which is hard to achieve. To resolve this issue, we introduce Predictive Context Priors by learning Predictive Queries for each specific point cloud at inference time. Specifically, we first train a local context prior using a large point cloud dataset similar to previous techniques. For surface reconstruction at inference time, however, we specialize the local context prior into our Predictive Context Prior by learning Predictive Queries, which predict adjusted spatial query locations as displacements of the original locations. This leads to a global SDF that fits the specific point cloud the best. Intuitively, the query prediction enables us to flexibly search the learned local context prior over the entire prior space, rather than being restricted to the fixed query locations, and this improves the generalizability. Our method does not require ground truth signed distances, normals, or any additional procedure of signed distance fusion across overlapping regions. Our experimental results in surface reconstruction for single shapes or complex scenes show significant improvements over the state-of-the-art under widely used benchmarks.

Related papers

• Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching [0.0]
  Point cloud matching is a crucial technique in computer vision, medical and robotics fields. We propose a new technique, based on graph Laplacian eigenmaps, to match point clouds by taking into account fine local structures. We show that the similarity between those aligned high-dimensional spaces provides a locally meaningful score to match shapes.
  arXiv  Detail & Related papers  (2024-02-27T10:10:12Z)
• CPR++: Object Localization via Single Coarse Point Supervision [55.8671776333499]
  coarse point refinement (CPR) is first attempt to alleviate semantic variance from an algorithmic perspective. CPR reduces semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point. CPR++ can obtain scale information and further reduce the semantic variance in a global region.
  arXiv  Detail & Related papers  (2024-01-30T17:38:48Z)
• Pre-training Contextual Location Embeddings in Personal Trajectories via Efficient Hierarchical Location Representations [30.493743596793212]
  Pre-training the embedding of a location generated from human mobility data has become a popular method for location based services. Previous studies have handled less than ten thousand distinct locations, which is insufficient in the real-world applications. We propose a Geo-Tokenizer, designed to efficiently reduce the number of locations to be trained by representing a location as a combination of several grids at different scales.
  arXiv  Detail & Related papers  (2023-10-02T14:40:24Z)
• Decoupled Local Aggregation for Point Cloud Learning [12.810517967372043]
  We propose to decouple the explicit modelling of spatial relations from local aggregation. We present DeLA, a lightweight point network, where in each learning stage relative spatial encodings are first formed. DeLA achieves over 90% overall accuracy on ScanObjectNN and 74% mIoU on S3DIS Area 5.
  arXiv  Detail & Related papers  (2023-08-31T08:21:29Z)
• SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space [50.14426188851305]
  We propose the first SE(3)-equivariant coordinate-based network for learning occupancy fields from point clouds. In contrast to previous shape reconstruction methods that align the input to a regular grid, we operate directly on the irregular, unoriented point cloud. We show that our method outperforms previous SO(3)-equivariant methods, as well as non-equivariant methods trained on SO(3)-augmented datasets.
  arXiv  Detail & Related papers  (2022-04-05T17:59:15Z)
• Sign-Agnostic CONet: Learning Implicit Surface Reconstructions by Sign-Agnostic Optimization of Convolutional Occupancy Networks [39.65056638604885]
  We learn implicit surface reconstruction by sign-agnostic optimization of convolutional occupancy networks. We show this goal can be effectively achieved by a simple yet effective design.
  arXiv  Detail & Related papers  (2021-05-08T03:35:32Z)
• Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration [67.69257782645789]
  We propose piecewise transformation fields that learn 3D translation vectors to map any query point in posed space to its correspond position in rest-pose space. We show that fitting parametric models with poses by our network results in much better registration quality, especially for extreme poses.
  arXiv  Detail & Related papers  (2021-04-16T15:16:09Z)
• SIRI: Spatial Relation Induced Network For Spatial Description Resolution [64.38872296406211]
  We propose a novel relationship induced (SIRI) network for language-guided localization. We show that our method is around 24% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius. Our method also generalizes well on our proposed extended dataset collected using the same settings as Touchdown.
  arXiv  Detail & Related papers  (2020-10-27T14:04:05Z)
• Local Context Attention for Salient Object Segmentation [5.542044768017415]
  We propose a novel Local Context Attention Network (LCANet) to generate locally reinforcement feature maps in a uniform representational architecture. The proposed network introduces an Attentional Correlation Filter (ACF) module to generate explicit local attention by calculating the correlation feature map between coarse prediction and global context. Comprehensive experiments are conducted on several salient object segmentation datasets, demonstrating the superior performance of the proposed LCANet against the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-09-24T09:20:06Z)
• LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts [65.79931333193016]
  We present a novel Local-Region-Context Network (LRC-Net) to learn discriminative features on point clouds. LRC-Net encodes fine-grained contexts inside and among local regions simultaneously. Results show LRC-Net is competitive with state-of-the-art methods in shape classification and shape segmentation applications.
  arXiv  Detail & Related papers  (2020-03-18T14:34:08Z)

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.