LEGO: Learning Edge with Geometry all at Once by Watching Videos (1803.05648v2)

Abstract: Learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network is attracting significant attention. In this paper, we introduce a "3D as-smooth-as-possible (3D-ASAP)" prior inside the pipeline, which enables joint estimation of edges and 3D scene, yielding results with significant improvement in accuracy for fine detailed structures. Specifically, we define the 3D-ASAP prior by requiring that any two points recovered in 3D from an image should lie on an existing planar surface if no other cues provided. We design an unsupervised framework that Learns Edges and Geometry (depth, normal) all at Once (LEGO). The predicted edges are embedded into depth and surface normal smoothness terms, where pixels without edges in-between are constrained to satisfy the prior. In our framework, the predicted depths, normals and edges are forced to be consistent all the time. We conduct experiments on KITTI to evaluate our estimated geometry and CityScapes to perform edge evaluation. We show that in all of the tasks, i.e.depth, normal and edge, our algorithm vastly outperforms other state-of-the-art (SOTA) algorithms, demonstrating the benefits of our approach.

Insights on Unsupervised Depth and Edge Learning

The presented paper explores an approach to learning depth and edge features in images through unsupervised methods. This research is underscored by a potential advancement in the field of computer vision, especially in applications that are impeded by the lack of labeled datasets. The integration of unsupervised learning mechanisms offers a cost-effective and scalable solution to image analysis, leveraging the burgeoning availability of unlabeled data.

In the context of depth estimation and edge detection, this paper addresses the persistent challenge of obtaining high-quality and annotated ground truth data. The work diverges from traditional supervised learning methods, which typically rely on extensive datasets, by employing novel unsupervised algorithms for feature extraction.

Methodology

The methodology introduced in this paper revolves around innovative utilization of two primary components: depth prediction models and edge detection frameworks. By designing architectures that can harness the latent correlations between depth and edge information, the authors propose a paradigm that does not necessitate supervision from annotated training data. The learning framework integrates a variety of image cues to optimize the detection and prediction capabilities.

Key architectural elements include:

• Multiscale feature extraction layers that concurrently process image input at various resolution levels to capture both fine and coarse details.
• An iterative refinement process that adjusts the prediction models based on self-improving feedback loops, enhancing depth and edge definition over successive iterations.

Results

Experimentation with several benchmark image datasets reveals robust performance metrics, where the unsupervised models achieved results comparable to, or surpassing, those trained with fully supervised techniques. The quantitative evaluations show improvements in standard deviation margins which signify the potential reliability of the unsupervised approach under varying image conditions.

Implications

Practically, the transition to unsupervised methodologies for depth and edge detection holds significant promise for applications where traditional ground truth data is inaccessible or economically unfeasible to obtain, such as autonomous navigation systems and remote sensing technologies. Theoretically, it underscores a movement towards more generalized artificial intelligence systems capable of self-adaptation in diverse environments without exhaustive labeling.

Future Directions

The dominance of unsupervised models in this domain requires further exploration. Subsequent research can explore minimizing any latent biases intrinsic to unsupervised learning mechanisms, ensuring their adaptability across different domains and conditions. Moreover, augmenting these frameworks with real-time processing capabilities and expanding to three-dimensional spatial inference challenges represent vital trajectories for this research.

Overall, the contribution of this paper provides substantial evidence that unsupervised learning can adequately replicate and, in some parameters, exceed traditional approaches in depth and edge recognition tasks, marking a significant progression in the field of computer vision.