NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video (2104.00681v1)

Abstract: We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, we propose to directly reconstruct local surfaces represented as sparse TSDF volumes for each video fragment sequentially by a neural network. A learning-based TSDF fusion module based on gated recurrent units is used to guide the network to fuse features from previous fragments. This design allows the network to capture local smoothness prior and global shape prior of 3D surfaces when sequentially reconstructing the surfaces, resulting in accurate, coherent, and real-time surface reconstruction. The experiments on ScanNet and 7-Scenes datasets show that our system outperforms state-of-the-art methods in terms of both accuracy and speed. To the best of our knowledge, this is the first learning-based system that is able to reconstruct dense coherent 3D geometry in real-time.

• The paper introduces a novel neural framework that decomposes monocular video into fragments to directly reconstruct dense 3D surfaces with enhanced coherence.
• It employs gated recurrent units and sparse convolutions to predict local TSDF volumes, achieving temporal consistency and 33 key frames per second on ScanNet and 7-Scenes.
• The approach significantly improves reconstruction accuracy and speed, paving the way for immersive augmented reality applications and potential semantic integration.

Real-Time 3D Reconstruction with NeuralRecon: A Detailed Overview

The paper "NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video" introduces a novel framework that addresses the challenge of real-time, dense, and coherent 3D scene reconstruction from monocular video inputs. The researchers present a departure from traditional single-view depth map estimation methodologies, proposing instead a direct reconstruction of local surfaces using learning-based techniques to generate sparse TSDF (Truncated Signed Distance Function) volumes. This approach is designed to sequentially process video fragments via a neural network, facilitating efficient 3D reconstruction with enhanced accuracy and coherence.

Methodology

The framework is structured around sequential reconstruction of surface fragments utilizing a neural architecture composed of gated recurrent units (GRU) and sparse convolutional layers. Key innovations of the approach include:

1. Fragment-Based Reconstruction: Rather than processing entire sequences globally, NeuralRecon decomposes the video input into fragments, performing reconstruction operations locally. This design aids in managing computational complexity while ensuring consistency across surface estimates.
2. TSDF Volume Estimation: The method involves predicting the TSDF volume for each fragment, which complements the TSDF fusion process. The GRU-based fusion module integrates feature information from preceding fragments, ensuring temporal and spatial consistency in the reconstructed meshes.
3. Sparse Convolutional Network: By utilizing sparse convolutions, NeuralRecon efficiently processes 3D volumetric data, enabling the system to maintain real-time performance levels while handling relatively large inputs.

Experimental Evaluation

The framework's efficacy was validated using the ScanNet and 7-Scenes datasets, benchmarks that are prevalent in indoor scene reconstruction evaluation. The results indicated that NeuralRecon outperforms existing state-of-the-art methods concerning both accuracy and processing speed. Notably, NeuralRecon achieves unmatched runtime efficiency at 33 key frames per second, significantly surpassing alternative approaches including Atlas, a prominent offline volumetric reconstruction method.

Implications and Future Directions

NeuralRecon's ability to perform dense monocular reconstruction in real-time holds substantial practical implications, particularly in augmented reality (AR) environments where dynamic interaction with physical spaces is paramount. By allowing for instantaneous surface updates from video streams, this method facilitates more robust and immersive AR experiences.

Theoretically, this work propels advancements in real-time computer vision by effectively integrating neural networks with geometric reconstruction processes. The GRU-fusion module exemplifies an innovative application of recurrent neural networks to manage spatial-temporal dependencies in 3D data.

Future research could explore augmenting NeuralRecon with semantic understanding to enhance the interpretability and functionality of reconstructed scenes. Integrating these insights with other AI-driven systems presents opportunities to develop comprehensive models capable of unified spatial and semantic awareness. Furthermore, extending this work to outdoor and more complex indoor environments could pave the way for broader applications across various domains.

In conclusion, NeuralRecon presents a significant contribution to the field of computer vision and real-time 3D scene reconstruction, underscoring the potential for neural architectures to transform how three-dimensional data is processed and understood.