How useful is photo-realistic rendering for visual learning?
Data seems cheap to get, and in many ways it is, but the process of creating a high quality labeled dataset from a mass of data is time-consuming and expensive. With the advent of rich 3D repositories, photo-realistic rendering systems offer the opportunity to provide nearly limitless data. Yet, their primary value for visual learning may be the quality of the data they can provide rather than the quantity. Rendering engines offer the promise of perfect labels in addition to the data: what the precise camera pose is; what the precise lighting location, temperature, and distribution is; what the geometry of the object is. In this work we focus on semi-automating dataset creation through use of synthetic data and apply this method to an important task -- object viewpoint estimation. Using state-of-the-art rendering software we generate a large labeled dataset of cars rendered densely in viewpoint space. We investigate the effect of rendering parameters on estimation performance and show realism is important. We show that generalizing from synthetic data is not harder than the domain adaptation required between two real-image datasets and that combining synthetic images with a small amount of real data improves estimation accuracy.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationViewpoint EstimationSimilar Papers 제목 키워드 기반
Automatic Content-aware Non-Photorealistic Rendering of Images
Non-photorealistic rendering techniques work on image features and often manipulate a set of characteristics such as edges and texture to achieve a desired depiction of the scene. Most computational photography methods d…
BIM Hyperreality: Data Synthesis Using BIM and Hyperrealistic Rendering for Deep Learning
Deep learning is expected to offer new opportunities and a new paradigm for the field of architecture. One such opportunity is teaching neural networks to visually understand architectural elements from the built environ…
Generative Adversarial NetworkObject RecognitionBeyond Reconstruction: A Physics Based Neural Deferred Shader for Photo-realistic Rendering
Deep learning based rendering has demonstrated major improvements for photo-realistic image synthesis, applicable to various applications including visual effects in movies and photo-realistic scene building in video gam…
Image GenerationGaussianGAN: Real-Time Photorealistic controllable Human Avatars
Photorealistic and controllable human avatars have gained popularity in the research community thanks to rapid advances in neural rendering, providing fast and realistic synthesis tools. However, a limitation of current …
Semantic SegmentationTime-Archival Camera Virtualization for Sports and Visual Performances
Camera virtualization -- an emerging solution to novel view synthesis -- holds transformative potential for visual entertainment, live performances, and sports broadcasting by enabling the generation of photorealistic im…
Representation LearningNovel View SynthesisPoint Clouds