Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study the problem of 3D object generation.  propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets. The benefits of our model are three-fold: first, the use of an adversarial criterion, instead of traditional heuristic criteria, enables the generator to capture object structure implicitly and to synthesize high-quality 3D objects; second, the generator establishes a mapping from a low-dimensional probabilistic space to the space of 3D objects, so that  can sample objects without a reference image or CAD models, and explore the 3D object manifold; third, the adversarial discriminator provides a porful 3D shape descriptor which, learned without supervision, has wide applications in 3D object recognition. Experiments demonstrate that our method generates high-quality 3D objects, and our unsupervisedly learned features achieve impressive performance on 3D object recognition, comparable with those of supervised learning methods.
['Jiajun Wu', 'Chengkai Zhang', 'Tianfan Xue', 'William T. Freeman', 'Joshua B. Tenenbaum']
I am writing a research paper on a idea Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
[{"from": "human", "value": "I am writing a research paper on a idea Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study the problem of 3D object generation.  propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets. The benefits of our model are three-fold: first, the use of an adversarial criterion, instead of traditional heuristic criteria, enables the generator to capture object structure implicitly and to synthesize high-quality 3D objects; second, the generator establishes a mapping from a low-dimensional probabilistic space to the space of 3D objects, so that  can sample objects without a reference image or CAD models, and explore the 3D object manifold; third, the adversarial discriminator provides a porful 3D shape descriptor which, learned without supervision, has wide applications in 3D object recognition. Experiments demonstrate that our method generates high-quality 3D objects, and our unsupervisedly learned features achieve impressive performance on 3D object recognition, comparable with those of supervised learning methods."}]