EC-Net: an Edge-aware Point set Consolidation Network (1807.06010v1)

Abstract: Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds. We design our network to process points grouped in local patches, and train it to learn and help consolidate points, deliberately for edges. To achieve this, we formulate a regression component to simultaneously recover 3D point coordinates and point-to-edge distances from upsampled features, and an edge-aware joint loss function to directly minimize distances from output points to 3D meshes and to edges. Compared with previous neural network based works, our consolidation is edge-aware. During the synthesis, our network can attend to the detected sharp edges and enable more accurate 3D reconstructions. Also, we trained our network on virtual scanned point clouds, demonstrated the performance of our method on both synthetic and real point clouds, presented various surface reconstruction results, and showed how our method outperforms the state-of-the-arts.

• The paper introduces a novel edge-aware model that refines 3D point clouds by accurately preserving sharp edges.
• It employs a PointNet++-inspired feature embedding, edge distance regression, and coordinate refinement to enhance reconstruction quality.
• EC-Net outperforms state-of-the-art methods by using an edge-aware joint loss that optimizes surface alignment and point distribution.

Overview of EC-Net: An Edge-aware Point Set Consolidation Network

The paper "EC-Net: an Edge-aware Point Set Consolidation Network" presents an innovative approach for improving the quality of 3D point clouds obtained from scans. This research makes a significant contribution to the field of deep learning in geometric modeling by proposing a novel edge-aware technique specifically designed for point cloud consolidation.

Point cloud consolidation is a crucial step in 3D reconstruction tasks as scanned data often have issues such as sparseness, irregularity, and noise. Traditional geometric methods have addressed this with limited efficacy due to oversimplified priors and assumptions about smoothness that do not generalize well across all geometries.

Architecture and Methodology

EC-Net is a deep learning model that leverages local patches within point clouds to effectively learn and preserve sharp edges during consolidation. The key components of EC-Net’s architecture include:

1. Feature Embedding and Expansion: This adopts a PointNet++-inspired mechanism to transform raw point coordinates into meaningful deep feature representations. These features are then expanded to increase point density, an essential aspect for achieving higher reconstruction accuracy, especially near edges.
2. Edge Distance Regression: The network incorporates a module for regressing point-to-edge distances, which is crucial for edge-aware processing. This enables the network to prioritize the retention of sharp edge features in its output.
3. Coordinate Regression: EC-Net employs a regression module which refines the coordinates by learning residuals relative to input points. This approach helps in iterating the positions to converge closer to the true geometry of the scanned object.
4. Edge Points Identification: By leveraging the predicted point-to-edge distances, edge points are identified, allowing the network to reinforce consolidation efforts in these critical regions.

Edge-aware Joint Loss Function

The work introduces an edge-aware joint loss function comprising several components designed to optimize the network's sensitivity to surfaces and edges:

• Surface Loss encourages output points to align closely with the object's the underlying geometry.
• Edge Loss focuses on maintaining proximity of points to edges, making the approach edge-aware.
• Repulsion Loss ensures that points are evenly distributed, addressing non-uniformity issues often found in raw point scans.
• Edge Distance Regression Loss aids the network in accurately predicting distances to edges.

Experimental Results and Implications

The authors conducted extensive experiments comparing EC-Net against state-of-the-art methods such as PU-Net and EAR. The results indicate superior performance of EC-Net in preserving edge features and facilitating high-quality surface reconstructions from both synthetic and real scanned point clouds. This success is quantified through metrics like the Hausdorff distance, showcasing EC-Net's ability to produce points that closely adhere to the reference 3D models.

EC-Net's methodology, which involves virtual scanning to prepare training data, allows it to effectively simulate real-world scan conditions, including noise and non-uniformity. This enhancement significantly boosts the application potential in areas requiring precise 3D models, such as virtual reality, CAD modeling, and digital twin creation.

Future Directions

While EC-Net marks a significant advancement in edge-aware consolidation, future research could focus on expanding this approach to incorporate more complex scenarios, such as completing missing structures in point clouds. Exploring adaptive patch sizes and dynamic context incorporation could enhance its versatility. Moreover, further development can address the challenges in reconstructing under-sampled or extremely complex tiny structures.

In conclusion, EC-Net is an adept model that sets a new standard in point cloud processing with its specialized edge-aware capabilities. This opens up new avenues for more realistic and high-fidelity 3D reconstructions, contributing substantially to the fields of computer vision and computational geometry. The research presents promising prospects for innovating further in AI-driven geometrical modeling.