Convolutional Occupancy Networks
Convolutional Occupancy Networks (CON) combine convolutional encoders with implicit occupancy decoders, achieving high-precision 3D reconstruction of complex scenes.
Key Findings
Methodology
The proposed CON framework integrates convolutional encoders with implicit occupancy decoders, utilizing multi-plane and volumetric features. The encoder extracts local and global features from noisy point clouds or coarse voxel grids via task-specific networks like PointNet or 3D CNNs. Features are projected onto multiple planes or volumes, processed through 2D/3D U-Net architectures to encode spatial hierarchies. Query points are interpolated using bilinear or trilinear methods, then passed through a small fully connected network to predict occupancy probabilities. Training employs binary cross-entropy loss, enabling the model to learn detailed geometries from sparse or noisy inputs. The approach supports scene-level reconstruction with sliding window inference, scalable to large scenes.
Key Results
- On ShapeNet, the model achieves IoU of 0.884 from point cloud inputs, surpassing the baseline ONet's 0.761, with faster convergence and fewer parameters. In scene reconstruction, combining multi-plane and volumetric features yields IoU of 0.805 on synthetic indoor datasets, outperforming single-feature variants. The model generalizes well to real-world datasets, with IoU reaching 0.849 on ScanNet and Matterport3D, producing smooth, detailed reconstructions.
- Ablation studies show that multi-scale feature fusion and bilinear interpolation significantly improve accuracy. The method maintains high performance across different resolutions and input noise levels, demonstrating robustness and scalability for large-scale scene understanding.
Significance
This work advances implicit neural representations by embedding convolutional inductive biases, enabling detailed, scalable 3D scene reconstruction. It addresses the challenge of capturing complex geometries in large scenes, which previous methods struggled with due to fully connected architectures. The approach enhances the ability to reconstruct intricate details and supports real-world applications like virtual reality, robotics, and urban modeling. Its scalability and generalization capabilities mark a significant step toward practical deployment of neural implicit models in large-scale scene understanding.
Technical Contribution
The paper introduces a convolutional implicit occupancy framework that leverages multi-scale spatial features for high-fidelity 3D reconstruction. It innovates by integrating multi-plane and volumetric features processed through 2D/3D U-Net architectures, enabling hierarchical spatial encoding. The model supports sliding window inference for large scenes, reducing memory footprint while maintaining accuracy. The use of bilinear/trilinear interpolation for feature querying enhances detail preservation. This architecture surpasses prior fully-connected implicit models in scalability, efficiency, and detail fidelity, opening new avenues for scene-level neural reconstruction.
Novelty
This is the first work to embed convolutional inductive biases into implicit occupancy networks for large-scale scene reconstruction. Unlike prior methods limited to single objects or small scenes, CON employs multi-plane and volumetric features processed via convolutional U-Nets, enabling hierarchical spatial encoding. The approach supports scene-level inference with sliding windows, significantly improving scalability and detail preservation. It bridges the gap between local feature exploitation and continuous implicit representations, setting a new benchmark for 3D scene understanding.
Limitations
- Despite improvements, the model's performance degrades under extremely sparse or noisy point clouds, especially for intricate geometries. High-resolution features demand substantial computational resources, limiting real-time applications. The reliance on pre-defined canonical planes may restrict flexibility in highly irregular scenes. Further research is needed to enhance robustness, reduce inference time, and adapt to diverse input modalities.
Future Work
Future directions include integrating multi-modal data such as images and depth maps to improve robustness and detail. Developing adaptive multi-scale feature fusion strategies could further enhance accuracy and efficiency. Exploring self-supervised training paradigms may reduce dependence on labeled data. Extending the framework to dynamic scenes and real-time applications remains an open challenge, promising broader deployment in robotics, AR/VR, and urban modeling.
AI Executive Summary
Implicit neural representations have revolutionized 3D reconstruction, offering continuous, topology-agnostic shape modeling. However, existing methods predominantly rely on fully connected networks, which struggle to scale to large, complex scenes due to limited capacity to incorporate local spatial information. This bottleneck hampers detailed scene understanding necessary for applications like virtual reality, robotics, and urban planning.
Addressing this challenge, the authors propose Convolutional Occupancy Networks (CON), a novel framework that marries convolutional encoders with implicit occupancy decoders. By leveraging multi-plane and volumetric features processed through 2D and 3D U-Net architectures, CON encodes hierarchical spatial information, capturing both local details and global context. The key innovation lies in embedding translation equivariance and local self-similarity into the implicit representation, enabling the model to reconstruct intricate geometries from noisy point clouds and low-resolution voxel inputs.
Experimental results demonstrate the effectiveness of CON across multiple datasets. On ShapeNet, the model surpasses prior state-of-the-art methods, achieving an IoU of 0.884 from point clouds, with faster convergence and fewer parameters. In scene-level reconstruction tasks on synthetic indoor datasets, the model attains an IoU of 0.805, significantly better than baselines. Moreover, the model generalizes well to real-world datasets like ScanNet and Matterport3D, maintaining high accuracy and producing smooth, detailed reconstructions.
The broader impact of this work is substantial. It offers a scalable, efficient solution for large-scale scene understanding, bridging the gap between local feature exploitation and continuous implicit modeling. The approach paves the way for real-time, high-fidelity 3D reconstruction in practical applications, including autonomous navigation, AR/VR content creation, and urban modeling. Despite these advances, challenges remain in handling extreme noise, reducing computational costs, and extending to dynamic scenes. Future research will focus on multi-modal integration, adaptive multi-scale fusion, and self-supervised learning to further enhance robustness and efficiency, pushing the boundaries of neural implicit scene modeling.
Deep Dive
Abstract
Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction. While demonstrating promising results, most implicit approaches are limited to comparably simple geometry of single objects and do not scale to more complicated or large-scale scenes. The key limiting factor of implicit methods is their simple fully-connected network architecture which does not allow for integrating local information in the observations or incorporating inductive biases such as translational equivariance. In this paper, we propose Convolutional Occupancy Networks, a more flexible implicit representation for detailed reconstruction of objects and 3D scenes. By combining convolutional encoders with implicit occupancy decoders, our model incorporates inductive biases, enabling structured reasoning in 3D space. We investigate the effectiveness of the proposed representation by reconstructing complex geometry from noisy point clouds and low-resolution voxel representations. We empirically find that our method enables the fine-grained implicit 3D reconstruction of single objects, scales to large indoor scenes, and generalizes well from synthetic to real data.