NeurVPS: Neural Vanishing Point Scanning via Conic Convolution (1910.06316v3)

Abstract: We present a simple yet effective end-to-end trainable deep network with geometry-inspired convolutional operators for detecting vanishing points in images. Traditional convolutional neural networks rely on aggregating edge features and do not have mechanisms to directly exploit the geometric properties of vanishing points as the intersections of parallel lines. In this work, we identify a canonical conic space in which the neural network can effectively compute the global geometric information of vanishing points locally, and we propose a novel operator named conic convolution that can be implemented as regular convolutions in this space. This new operator explicitly enforces feature extractions and aggregations along the structural lines and yet has the same number of parameters as the regular 2D convolution. Our extensive experiments on both synthetic and real-world datasets show that the proposed operator significantly improves the performance of vanishing point detection over traditional methods. The code and dataset have been made publicly available at https://github.com/zhou13/neurvps.

• The paper NeurVPS introduces a geometry-inspired deep network leveraging conic convolution for robust and precise vanishing point detection in images.
• It utilizes novel conic convolution operators within a canonical conic space to directly exploit the geometric properties of structural lines, enhancing detection accuracy.
• Experimental results demonstrate NeurVPS consistently achieves superior precision on both synthetic and real-world datasets compared to existing methods, highlighting its potential for applications in 3D reconstruction and navigation.

NeurVPS: Neural Vanishing Point Scanning via Conic Convolution

The paper "NeurVPS: Neural Vanishing Point Scanning via Conic Convolution" introduces an innovative approach to vanishing point detection, a pivotal challenge in the domain of 3D computer vision. Using a geometry-inspired deep network architecture, NeurVPS leverages conic convolution operators to enhance the precision and robustness of vanishing point detection in images. This paper makes significant strides over traditional methods by introducing mechanisms that fully exploit the geometric properties of vanishing points.

Methodology

The authors articulate a novel convolutional operator termed as "conic convolution," which works within a defined canonical conic space. By operating in this space, the network shifts the focus from typical line detectors to a more intrinsic understanding of the geometric structures, i.e., the structural lines. This is achieved without increasing the parameter count compared to conventional 2D convolutions. The approach involves sampling candidate vanishing points and assessing their validity based on their alignment with line structures within the conic space, accounting for both local orientations and global geometric configurations.

The network architecture is composed of a backbone for feature extraction and a dedicated conic convolutional sub-network designed for candidate evaluation. This structured design allows for a higher accuracy in vanishing point detection, as confirmed by the rigorous testing conducted on both synthetic and real-world datasets.

Experimental Results

In extensive experiments, the performance of the NeurVPS was compared against several baselines, including traditional line detection methods and other neural network-based approaches. NeurVPS consistently demonstrated superior precision. For instance, on the SU3 Wireframe dataset, NeurVPS showed substantial increases in angle accuracy across various thresholds compared to conventional edge and line-based methodologies like LSD with J-linkage clustering. This indicates the efficacy of integrating geometric reasoning into the network architecture.

In real-world datasets such as Natural Scene and ScanNet, NeurVPS proved to outperform other methods, including those which involve preprocessing stages like horizon localization. This paper distinguishes NeurVPS as a self-sufficient system that can operate independently of external line detectors, thus marking its robustness and adaptability to different scene conditions.

Implications and Future Directions

The introduction of conic convolution in vanishing point detection represents a noteworthy advancement in how geometry-related tasks are handled within neural networks. This method's ability to directly leverage structural line information has significant implications for numerous applications, including 3D reconstruction, camera calibration, and autonomous navigation systems.

Theoretically, the findings open pathways for exploring further integration of geometric insights into deep learning architectures, which could extend beyond vanishing point determination. Practically, NeurVPS sets a precedent for developing more accurate and stable vision-based systems, crucial for environments requiring high reliability and precision.

Future developments may focus on optimizing the conic convolution operators for efficiency and exploring their applications in unsupervised environments or in domains with scarce labeled data. The flexibility and performance of this approach suggest broad applicability and potential improvements to many geometric reasoning tasks within artificial intelligence.