  Recent advances in neural radiance fields (NeRFs) achieve state-of-the-art
novel view synthesis and facilitate dense estimation of scene properties.
However, NeRFs often fail for large, unbounded scenes that are captured under
very sparse views with the scene content concentrated far away from the camera,
as is typical for field robotics applications. In particular, NeRF-style
algorithms perform poorly: (1) when there are insufficient views with little
pose diversity, (2) when scenes contain saturation and shadows, and (3) when
finely sampling large unbounded scenes with fine structures becomes
computationally intensive.
  This paper proposes CLONeR, which significantly improves upon NeRF by
allowing it to model large outdoor driving scenes that are observed from sparse
input sensor views. This is achieved by decoupling occupancy and color learning
within the NeRF framework into separate Multi-Layer Perceptrons (MLPs) trained
using LiDAR and camera data, respectively. In addition, this paper proposes a
novel method to build differentiable 3D Occupancy Grid Maps (OGM) alongside the
NeRF model, and leverage this occupancy grid for improved sampling of points
along a ray for volumetric rendering in metric space.
  Through extensive quantitative and qualitative experiments on scenes from the
KITTI dataset, this paper demonstrates that the proposed method outperforms
state-of-the-art NeRF models on both novel view synthesis and dense depth
prediction tasks when trained on sparse input data.
