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This work explores the use of 3D generative models to synthesize trainingdata for 3D vision tasks. The key requirements of the generative models arethat the generated data should be photorealistic to match the real-worldscenarios, and the corresponding 3D attributes should be aligned with givensampling labels. However, we find that the recent NeRF-based 3D GANs hardlymeet the above requirements due to their designed generation pipeline and thelack of explicit 3D supervision. In this work, we propose Lift3D, an inverted2D-to-3D generation framework to achieve the data generation objectives. Lift3Dhas several merits compared to prior methods: (1) Unlike previous 3D GANs thatthe output resolution is fixed after training, Lift3D can generalize to anycamera intrinsic with higher resolution and photorealistic output. (2) Bylifting well-disentangled 2D GAN to 3D object NeRF, Lift3D provides explicit 3Dinformation of generated objects, thus offering accurate 3D annotations fordownstream tasks. We evaluate the effectiveness of our framework by augmentingautonomous driving datasets. Experimental results demonstrate that our datageneration framework can effectively improve the performance of 3D objectdetectors. Project page: https://len-li.github.io/lift3d-web.

Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field