Search Results for author:
Found 10 papers, 2 papers with code
Stable Surface Regularization for Fast Few-Shot NeRF
This paper proposes an algorithm for synthesizing novel views under few-shot setup.
Spacetime Surface Regularization for Neural Dynamic Scene Reconstruction
We propose an algorithm, 4DRegSDF, for the spacetime surface regularization to improve the fidelity of neural rendering and reconstruction in dynamic scenes.
MATE: Masked Autoencoders are Online 3D Test-Time Learners
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data.
Facial Depth and Normal Estimation using Single Dual-Pixel Camera
Many mobile manufacturers recently have adopted Dual-Pixel (DP) sensors in their flagship models for faster auto-focus and aesthetic image captures.
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
Inspired by recent multi-modal UDA techniques, the proposed method exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain pose labels.
Ranked #5 on
6D Pose Estimation using RGBD
on REAL275
Deep Point Cloud Reconstruction
In particular, we further improve the performance of transformer by a newly proposed module called amplified positional encoding.
PointMixer: MLP-Mixer for Point Cloud Understanding
MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer.
Ranked #20 on
Semantic Segmentation
on S3DIS Area5
VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction
To reconstruct a 3D scene from a set of calibrated views, traditional multi-view stereo techniques rely on two distinct stages: local depth maps computation and global depth maps fusion.
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation
The key idea of our network is to exploit sparse and accurate point clouds as a cue for guiding correspondences of stereo images in a unified 3D volume space.
Stereo Object Matching Network
This paper presents a stereo object matching method that exploits both 2D contextual information from images as well as 3D object-level information.
