Neural Contours: Learning to Draw Lines from 3D Shapes (2003.10333v3)

Abstract: This paper introduces a method for learning to generate line drawings from 3D models. Our architecture incorporates a differentiable module operating on geometric features of the 3D model, and an image-based module operating on view-based shape representations. At test time, geometric and view-based reasoning are combined with the help of a neural module to create a line drawing. The model is trained on a large number of crowdsourced comparisons of line drawings. Experiments demonstrate that our method achieves significant improvements in line drawing over the state-of-the-art when evaluated on standard benchmarks, resulting in drawings that are comparable to those produced by experienced human artists.

Summary of "Neural Contours: Learning to Draw Lines from 3D Shapes"

The paper "Neural Contours: Learning to Draw Lines from 3D Shapes" presents a novel method for generating line drawings from 3D models by leveraging both geometric and neural network approaches. Unlike traditional techniques that rely solely on the geometric properties of 3D models or view-based stylization networks that lack 3D shape awareness, this method amalgamates these two paradigms to harness their complementary strengths.

Methodology

The proposed method employs a dual-branch architecture comprising a geometry branch and an image translation branch. The geometry branch builds upon traditional geometric line drawing techniques, such as suggestive contours, apparent ridges, and ridges and valleys, using a differentiable framework. This branch addresses the limitation of prior techniques that require manual parameter tuning for each model by autonomously learning optimal parameters.

The image translation branch adapts the paradigms of image-to-image translation networks. It inputs multi-view representations of the shape, including depth and various smoothed shaded images. It refines the line drawing process by learning effective mappings from these view-based representations.

A significant innovation is the incorporation of a Neural Ranking Module (NRM) to optimize the line drawing parameters in the geometric branch and to ensure the best synthesis of both branches' outputs. This module uses a neural network to discern the plausibility of generated line drawings, facilitating the optimization process to enhance output quality actively.

Results and Evaluation

The method shows considerable advancements over existing approaches, including both geometric and image-based methods. Evaluated primarily on datasets derived from artist-generated drawings, the method's outputs exhibit higher fidelity in capturing human drawing nuances, demonstrating improved Intersection over Union (IoU) and Chamfer Distance metrics.

Notably, the method reflects a notable improvement in user perception, doubling the preference rate in user studies over the next best alternative. The paper's approach was rigorously benchmarked against numerous baselines, including occluding contours and contemporary style transfer networks, with consistent results indicating superior accuracy and artistic resemblance.

Implications and Future Directions

The implications of this research extend to fields such as animation, virtual reality, and computer graphics, where precise line drawings are crucial for shape interpretation. The unification of geometric and neural-based models offers a nuanced understanding of artistic line drawing that addresses the shortcomings of their individual counterparts.

The introduction of a test-time optimization strategy for parameter tuning highlights a significant methodological advancement, providing a blueprint for future research in integrating perception-driven network modules. Future work could explore extending these methodologies to unstructured datasets like point clouds, thereby broadening the applicability of the proposed approach.

In conclusion, this paper presents a comprehensive and innovative method for line drawing, effectively bridging the gap between traditional geometry-based methods and modern neural models. The results underscore the potential of hybrid models to transform artistic rendering processes in computational fields.