Abstract 3D occupancy prediction is an important task for the robustness of vision-centric
autonomous driving which aims to predict whether each point is occupied in the surrounding …

Inferring a meaningful geometric scene representation from a single image is a fundamental
problem in computer vision. Approaches based on traditional depth map prediction can only …

The introduction of neural radiance fields has greatly improved the effectiveness of view
synthesis for monocular videos. However, existing algorithms face difficulties when dealing …

This paper tackles the challenges of self-supervised monocular depth estimation in indoor
scenes caused by large rotation between frames and low texture. We ease the learning …

Monocular Depth Estimation (MDE) is a critical component in applications such as
autonomous driving. There are various attacks against MDE networks. These attacks …

Inferring scene geometry from images via Structure from Motion is a long-standing and
fundamental problem in computer vision. While classical approaches and more recently …

Neural Radiance Fields (NeRF) has demonstrated remarkable 3D reconstruction
capabilities with dense view images. However, its performance significantly deteriorates …

Depth estimation plays an important role in robotic perception systems. The self-supervised
monocular paradigm has gained significant attention since it can free training from the …

In this paper we address single image-based novel view synthesis (NVS) by firstly
integrating view-dependent effects (VDE) into the process. Our approach leverages camera …

Robotic research encounters a significant hurdle when it comes to the intricate task of
grasping objects that come in various shapes, materials, and textures. Unlike many prior …