Learning-based perception and prediction modules in modern autonomous driving systems
typically rely on expensive human annotation and are designed to perceive only a handful of …

Neural implicit fields have recently emerged as a useful representation for 3D shapes.
These fields are commonly represented as neural networks which map latent descriptors …

The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

Abstract Neural Radiance Fields (NeRF) coupled with GANs represent a promising direction
in the area of 3D reconstruction from a single view, owing to their ability to efficiently model …

We present a new framework to learn dense 3D reconstruction and correspondence from a
single 2D image. The shape is represented implicitly as deformation over a category-level …

We present simultaneous generation and map** (SGAM), a novel 3D scene generation
algorithm. Our goal is to produce a realistic, globally consistent 3D world on a large scale …

In the last decade, deep learning (DL) has significantly impacted industry and science.
Initially largely motivated by computer vision tasks in 2-D imagery, the focus has shifted …

Research into dynamic 3D scene understanding has primarily focused on short-term change
tracking from dense observations while little attention has been paid to long-term changes …

Signed Distance Fields (SDFs) parameterized by neural networks have recently gained
popularity as a fundamental geometric representation. However, editing the shape encoded …

Realistic simulation is key to enabling safe and scalable development of% self-driving
vehicles. A core component is simulating the sensors so that the entire autonomy system …