We address the problem of synthesizing novel views from a monocular video depicting a
complex dynamic scene. State-of-the-art methods based on temporally varying Neural …

Abstract 3D reconstruction of deformable (or non‐rigid) scenes from a set of monocular 2D
image observations is a long‐standing and actively researched area of computer vision and …

In this paper we present a new method for deformable NeRF that can directly use optical
flow as supervision. We overcome the major challenge with respect to the computationally …

Abstract Neural Scene Flow Prior (NSFP) is of significant interest to the vision community
due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with …

We tackle semi-supervised object detection based on motion cues. Recent results suggest
that heuristic-based clustering methods in conjunction with object trackers can be used to …

Reconstructing models of the real world, including 3D geometry, appearance, and motion of
real scenes, is essential for computer graphics and computer vision. It enables the …

In recent years, there has been a significant increase in focus on the interpolation task of
computer vision. Despite the tremendous advancement of video interpolation, point cloud …

Understanding the motion behavior of dynamic environments is vital for autonomous driving,
leading to increasing attention in class-agnostic motion prediction in LiDAR point clouds …

The test-time optimization of scene flow—using a coordinate network as a neural prior [27]—
has gained popularity due to its simplicity, lack of dataset bias, and state-of-the-art …

Deep neural networks (DNNs) are increasingly integrated into LiDAR (Light Detection and
Ranging)-based perception systems for autonomous vehicles (AVs), requiring robust …