Search Results for author:
Found 12 papers, 6 papers with code
Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations
To this end, we explore the feasibility of self-supervised motion prediction with only unlabeled LiDAR point clouds.
DyBluRF: Dynamic Neural Radiance Fields from Blurry Monocular Video
Experimental results on our dataset demonstrate that our method outperforms existing approaches in generating sharp novel views from motion-blurred inputs while maintaining spatial-temporal consistency of the scene.
S-DyRF: Reference-Based Stylized Radiance Fields for Dynamic Scenes
To address this limitation, we present S-DyRF, a reference-based spatio-temporal stylization method for dynamic neural radiance fields.
Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
To improve the quality of pseudo labels, we propose a novel motion selection and re-generation module.
Make-It-4D: Synthesizing a Consistent Long-Term Dynamic Scene Video from a Single Image
To animate the visual content, the feature point cloud is displaced based on the scene flow derived from motion estimation and the corresponding camera pose.
SAD: Segment Any RGBD
The Segment Anything Model (SAM) has demonstrated its effectiveness in segmenting any part of 2D RGB images.
Open Vocabulary Semantic Segmentation
Panoptic Segmentation
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3D Cinemagraphy from a Single Image
To animate the scene, we perform motion estimation and lift the 2D motion into the 3D scene flow.
SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis
We study the problem of novel view synthesis of objects from a single image.
DoF-NeRF: Depth-of-Field Meets Neural Radiance Fields
To mitigate this issue, we introduce DoF-NeRF, a novel neural rendering approach that can deal with shallow DoF inputs and can simulate DoF effect.
Less is More: Consistent Video Depth Estimation with Masked Frames Modeling
Temporal consistency is the key challenge of video depth estimation.
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions
This paper investigates different variants of PointConv, a convolution network on point clouds, to examine their robustness to input scale and rotation changes.
