GINA-3D: Learning to Generate Implicit Neural Assets in the Wild

• URL: http://arxiv.org/abs/2304.02163v2
• Date: Mon, 28 Aug 2023 06:03:39 GMT
• Title: GINA-3D: Learning to Generate Implicit Neural Assets in the Wild
• Authors: Bokui Shen, Xinchen Yan, Charles R. Qi, Mahyar Najibi, Boyang Deng, Leonidas Guibas, Yin Zhou, Dragomir Anguelov
• Abstract summary: GINA-3D is a generative model that uses real-world driving data from camera and LiDAR sensors to create 3D implicit neural assets of diverse vehicles and pedestrians. We construct a large-scale object-centric dataset containing over 1.2M images of vehicles and pedestrians. We demonstrate that it achieves state-of-the-art performance in quality and diversity for both generated images and geometries.
• Score: 38.51391650845503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving. However, manually creating or re-creating real-world-like environments is difficult, expensive, and not scalable. Recent generative model techniques have shown promising progress to address such challenges by learning 3D assets using only plentiful 2D images -- but still suffer limitations as they leverage either human-curated image datasets or renderings from manually-created synthetic 3D environments. In this paper, we introduce GINA-3D, a generative model that uses real-world driving data from camera and LiDAR sensors to create realistic 3D implicit neural assets of diverse vehicles and pedestrians. Compared to the existing image datasets, the real-world driving setting poses new challenges due to occlusions, lighting-variations and long-tail distributions. GINA-3D tackles these challenges by decoupling representation learning and generative modeling into two stages with a learned tri-plane latent structure, inspired by recent advances in generative modeling of images. To evaluate our approach, we construct a large-scale object-centric dataset containing over 1.2M images of vehicles and pedestrians from the Waymo Open Dataset, and a new set of 80K images of long-tail instances such as construction equipment, garbage trucks, and cable cars. We compare our model with existing approaches and demonstrate that it achieves state-of-the-art performance in quality and diversity for both generated images and geometries.

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