Learning accurate and parsimonious point cloud representations of scene surfaces from
scratch remains a challenge in 3D representation learning. Existing point-based methods …

Photorealistic simulation plays a crucial role in applications such as autonomous driving
where advances in neural radiance fields (NeRFs) may allow better scalability through the …

In recent years, 3D Gaussian splatting has emerged as a powerful technique for 3D
reconstruction and generation, known for its fast and high-quality rendering capabilities. To …

In this paper we address the problem of efficient point searching and sampling for volume
neural rendering. Within this realm two typical approaches are employed: rasterization and …

Point cloud is a critical 3D representation with many emerging applications. Because of the
point sparsity and irregularity high-quality rendering of point clouds is challenging and often …

Recently, arbitrary-scale point cloud upsampling mechanism became increasingly popular
due to its efficiency and convenience for practical applications. To achieve this, most …

We investigate the benefit of combining blind audio recordings with 3D scene information for
novel-view acoustic synthesis. Given audio recordings from 2-4 microphones and the 3D …

Point clouds offer an attractive source of information to complement images in neural scene
representations, especially when few images are available. Neural rendering methods …

We study the problem of aligning a video that captures a local portion of an environment to
the 2D LiDAR scan of the entire environment. We introduce a method (VioLA) that starts with …

Unsigned distance functions (UDFs) have been a vital representation for open surfaces.
With different differentiable renderers, current methods are able to train neural networks to …