SimpleRecon: 3D Reconstruction Without 3D Convolutions (2208.14743v1)

Abstract: Traditionally, 3D indoor scene reconstruction from posed images happens in two phases: per-image depth estimation, followed by depth merging and surface reconstruction. Recently, a family of methods have emerged that perform reconstruction directly in final 3D volumetric feature space. While these methods have shown impressive reconstruction results, they rely on expensive 3D convolutional layers, limiting their application in resource-constrained environments. In this work, we instead go back to the traditional route, and show how focusing on high quality multi-view depth prediction leads to highly accurate 3D reconstructions using simple off-the-shelf depth fusion. We propose a simple state-of-the-art multi-view depth estimator with two main contributions: 1) a carefully-designed 2D CNN which utilizes strong image priors alongside a plane-sweep feature volume and geometric losses, combined with 2) the integration of keyframe and geometric metadata into the cost volume which allows informed depth plane scoring. Our method achieves a significant lead over the current state-of-the-art for depth estimation and close or better for 3D reconstruction on ScanNet and 7-Scenes, yet still allows for online real-time low-memory reconstruction. Code, models and results are available at https://nianticlabs.github.io/simplerecon

• The paper introduces a novel 3D reconstruction approach that replaces 3D convolutions with a refined 2D CNN-based depth estimation using plane-sweep features and geometric losses.
• It leverages keyframe and geometric metadata in cost volume integration to enhance multi-view depth fusion and enable real-time performance.
• Evaluations on ScanNet and 7-Scenes demonstrate significant improvements in reconstruction quality and computational efficiency compared to traditional methods.

"SimpleRecon: 3D Reconstruction Without 3D Convolutions" addresses a critical challenge in 3D indoor scene reconstruction by revisiting traditional methodologies while integrating modern enhancements to balance accuracy and efficiency.

Problem Overview

Traditional 3D reconstruction from posed images typically involves two main phases:

1. Per-image Depth Estimation: Depth information is extracted from individual images.
2. Depth Merging and Surface Reconstruction: The individual depth maps are fused to create a coherent 3D surface.

Recent trends have moved towards directly reconstructing scenes in the final 3D volumetric feature space using 3D convolutional layers, which can deliver excellent reconstruction results but at the cost of significant computational resources. These methods are often unsuitable for resource-constrained environments, necessitating a return to more efficient approaches.

Key Contributions

This paper introduces a 3D reconstruction methodology that:

1. Develops a High-Quality Multi-View Depth Estimator Using a 2D CNN:
   • Utilization of Strong Image Priors: Leverages plane-sweep feature volumes and geometric losses.
   • Keyframe and Geometric Metadata Integration: Enhances the cost volume for better depth plane scoring.
2. Focuses on Depth Estimation and Fusion, Avoiding 3D Convolutions:
   • By refining the depth estimation phase, the method achieves high fidelity in depth maps.
   • Uses simple, off-the-shelf depth fusion techniques to assemble the final 3D model efficiently.

Methodology

The proposed method employs a carefully designed 2D CNN, which brings together powerful image processing techniques and geometric insights to outperform conventional depth estimation algorithms. The paper emphasizes two main technical components:

1. 2D CNN with Plane-Sweep Feature Volume:
   • Strong image priors are employed through a 2D convolutional neural network.
   • Geometric losses are incorporated to enhance depth prediction accuracy.
2. Integration of Keyframe and Geometric Metadata:
   • Keyframe and additional geometric information are fused within the cost volume.
   • Enhanced cost volumes lead to improved depth plane scoring.

Performance and Comparisons

The effectiveness of SimpleRecon is demonstrated through extensive evaluations on well-regarded datasets such as ScanNet and 7-Scenes. The results show:

• Significantly Improved Depth Estimation: The method substantially outperforms existing state-of-the-art techniques in multi-view depth estimation.
• Comparable or Superior 3D Reconstruction: Achieves comparable or superior results in 3D reconstruction tasks without the need for computationally expensive 3D convolutions.
• Efficiency and Real-Time Capability: SimpleRecon facilitates online, real-time reconstruction with low memory usage, making it practical for use in constrained environments.

Implications

By focusing on a refined approach to depth estimation and efficient depth fusion techniques, SimpleRecon opens new avenues for real-time 3D reconstruction without the resource-intensive demands of 3D convolutions. This approach makes it particularly suitable for applications in augmented reality (AR), virtual reality (VR), and mobile robotics where computational resources are often limited.

In summary, "SimpleRecon" showcases a robust alternative to the 3D convolution-based methods, presenting a solution that strikes a balance between high-quality reconstructions and system efficiency.