Abstract 3D scene representations have gained immense popularity in recent years.
Methods that use Neural Radiance fields are versatile for traditional tasks such as novel …

We present a method for inferring dense depth from a camera image and a sparse noisy
radar point cloud. We first describe the mechanics behind mmWave radar point cloud …

Recovering dense and long-range pixel motion in videos is a challenging problem. Part of
the difficulty arises from the 3D-to-2D projection process leading to occlusions and …

We present" SemCity" a 3D diffusion model for semantic scene generation in real-world
outdoor environments. Most 3D diffusion models focus on generating a single object …

We present a significant breakthrough in 3D shape generation by scaling it to
unprecedented dimensions. Through the adaptation of the Auto-Regressive model and the …

Neural scene representations have achieved great success in parameterizing and
reconstructing images, but current state of the art models are not optimized with the …

We propose a novel concept of dual and integrated latent topologies (DITTO in short) for
implicit 3D reconstruction from noisy and sparse point clouds. Most existing methods …

Robotic manipulation is critical for admitting robotic agents to various application domains,
like intelligent assistance. A major challenge therein is the effective 6DoF grasping of …

Implicit neural networks have emerged as a crucial technology in 3D surface reconstruction.
To reconstruct continuous surfaces from discrete point clouds, encoding the input points into …

Implicit 3D surface reconstruction of an object from its partial and noisy 3D point cloud scan
is the classical geometry processing and 3D computer vision problem. In the literature …