Convolutional Occupancy Networks (2003.04618v2)

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.

• The paper introduces a novel method that combines convolutional encoders with implicit occupancy decoders for scalable and detailed 3D reconstruction.
• It employs planar and volumetric U-Net architectures to process noisy point clouds and coarse occupancy grids efficiently.
• The approach outperforms traditional models in metrics like IoU and Chamfer-L1, demonstrating robust performance on both synthetic and real-world datasets.

Convolutional Occupancy Networks: A New Approach for 3D Reconstruction

The paper "Convolutional Occupancy Networks" by Songyou Peng et al. introduces a novel methodology aimed at improving the state of 3D reconstruction using implicit representations. While implicit neural representations, such as Occupancy Networks, have shown efficacy in the accurate 3D reconstruction of single objects, they face limitations when applied to larger, more complex scenes. The authors address these limitations by incorporating convolutional operations into implicit occupancy decoders, leading to a more scalable and detailed generation of 3D geometries.

Methodology

Problem Formulation

The research centers on overcoming the inefficacies tied to the fully-connected network architectures used in traditional implicit models. These models lack mechanisms for structured reasoning in 3D space and fail to integrate local information effectively. Furthermore, they do not incorporate inductive biases like translational equivariance. To counter these issues, the authors propose a combination of convolutional encoders with implicit occupancy decoders.

Encoder and Decoder Architecture

The proposed method utilizes convolutional operations, which are inherently translational equivariant, to encode input data into feature representations (either planar or volumetric). Two types of inputs are considered in this work: noisy point clouds and coarse occupancy grids.

1. Plane Encoder: Input points are projected orthographically onto canonical planes which are then processed using 2D convolutional U-Nets.
2. Volume Encoder: Input features are aggregated into volumetric grids processed using 3D convolutional U-Nets.

The encoded features are subsequently decoded using interpolative methods to predict the occupancy probability of any given point in 3D space. Bilinear or trilinear interpolation techniques are employed to query the feature values, leading to robust occupancy predictions.

Results and Analysis

The performance of Convolutional Occupancy Networks was evaluated across several datasets, namely ShapeNet, a synthetic indoor scene dataset, and real-world datasets such as ScanNet and Matterport 3D.

Object-Level Reconstruction

For object-level reconstruction from noisy point clouds and low-resolution voxel grids, the proposed model significantly outperformed existing approaches such as Occupancy Networks (ONet) and PointConv across all tested metrics. Notably, the multi-plane projection approach achieved higher Intersection over Union (IoU) and Chamfer-L1 scores while maintaining lower computational requirements compared to volumetric representations.

Scene-Level Reconstruction

In synthetic indoor scenes, the convolutional model was adept at capturing intricate geometries and smoothly reconstructing scenes from point clouds. Combining planar and volumetric features yielded finer geometric details than considering each individually. The multi-planar approach provided a more computationally efficient solution while retaining high reconstruction accuracy.

Generalization to Real-World Data

When tested on ScanNet and Matterport3D, which contain real-world room scans, the model demonstrated strong generalization despite being trained only on synthetic data. The volumetric model particularly excelled, delivering smoother reconstructions and handling real-world noise more effectively than plan-based models.

Implications and Future Work

The research provides compelling evidence that convolutional operations enhance the capacity of implicit 3D representations to handle large-scale and complex scenes. This methodological advancement opens avenues for various applications, including but not limited to, indoor scene understanding, virtual reality, and robotic perception.

The future work as hypothesized by the authors could revolve around:

1. Expanding the model’s ability to handle rotational equivariance.
2. Improving performance disparity between synthetic and real data.
3. Potentially extending the principle of convolutional occupancy networks to other domains such as texture modeling and dynamic (4D) surface reconstruction.

This research introduces a clear pathway towards more detailed, accurate, and scalable 3D reconstructions, making it a significant addition to the field of computer vision.