  Reconstructing objects from real world data and rendering them at novel views
is critical to bringing realism, diversity and scale to simulation for robotics
training and testing. In this work, we present NeuSim, a novel approach that
estimates accurate geometry and realistic appearance from sparse in-the-wild
data captured at distance and at limited viewpoints. Towards this goal, we
represent the object surface as a neural signed distance function and leverage
both LiDAR and camera sensor data to reconstruct smooth and accurate geometry
and normals. We model the object appearance with a robust physics-inspired
reflectance representation effective for in-the-wild data. Our experiments show
that NeuSim has strong view synthesis performance on challenging scenarios with
sparse training views. Furthermore, we showcase composing NeuSim assets into a
virtual world and generating realistic multi-sensor data for evaluating
self-driving perception models.
