GenAssets: Generating in-the-wild 3D Assets in Latent Space

TL;DR

This paper introduces GenAssets, a 3D latent diffusion model that generates diverse, high-quality 3D assets from in-the-wild LiDAR and camera data, improving simulation realism.

Contribution

It proposes a novel reconstruct-then-generate approach using occlusion-aware neural rendering and latent diffusion to produce complete 3D assets from limited in-the-wild data.

Findings

Outperforms existing reconstruction and generation methods.

Produces diverse, complete 3D assets suitable for simulation.

Leverages occlusion-aware neural rendering for high-quality latent space.

Abstract

High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of autonomy. Building assets from in-the-wild data is key for diversity and realism, but existing neural-rendering based reconstruction methods are slow and generate assets that render well only from viewpoints close to the original observations, limiting their usefulness in simulation. Recent diffusion-based generative models build complete and diverse assets, but perform poorly on in-the-wild driving scenes, where observed actors are captured under sparse and limited fields of view, and are partially occluded. In this work, we propose a 3D latent diffusion model that learns on in-the-wild LiDAR and camera data captured by a sensor platform and generates high-quality 3D assets with complete geometry and appearance. Key to our method is a "reconstruct-then-generate" approach that first leverages occlusion-aware neural rendering trained over multiple scenes to build a high-quality latent space for objects, and then trains a diffusion model that operates on the latent space. We show our method outperforms existing reconstruction and generation based methods, unlocking diverse and scalable content creation for simulation.