Search Results for author: Shuwei Shao

Found 12 papers, 8 papers with code

Digging into contrastive learning for robust depth estimation with diffusion models

no code implementations15 Apr 2024 Jiyuan Wang, Chunyu Lin, Lang Nie, Kang Liao, Shuwei Shao, Yao Zhao

In this paper, we propose a novel robust depth estimation method called D4RD, featuring a custom contrastive learning mode tailored for diffusion models to mitigate performance degradation in complex environments.

 Ranked #1 on Unsupervised Monocular Depth Estimation on KITTI-C (using extra training data)

Contrastive Learning Denoising +3

$\mathrm{F^2Depth}$: Self-supervised Indoor Monocular Depth Estimation via Optical Flow Consistency and Feature Map Synthesis

no code implementations27 Mar 2024 Xiaotong Guo, Huijie Zhao, Shuwei Shao, Xudong Li, Baochang Zhang

To evaluate the generalization ability of our $\mathrm{F^2Depth}$, we collect a Campus Indoor depth dataset composed of approximately 1500 points selected from 99 images in 18 scenes.

Indoor Monocular Depth Estimation Monocular Depth Estimation +2

MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model

1 code implementation13 Nov 2023 Shuwei Shao, Zhongcai Pei, Weihai Chen, Dingchi Sun, Peter C. Y. Chen, Zhengguo Li

Because the depth ground-truth is unavailable in the training phase, we develop a pseudo ground-truth diffusion process to assist the diffusion in MonoDiffusion.

Denoising Monocular Depth Estimation

NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion

1 code implementation13 Nov 2023 Shuwei Shao, Zhongcai Pei, Weihai Chen, Peter C. Y. Chen, Zhengguo Li

To this end, we develop a normal-distance head that outputs pixel-level surface normal and distance.

Monocular Depth Estimation

IEBins: Iterative Elastic Bins for Monocular Depth Estimation

1 code implementation NeurIPS 2023 Shuwei Shao, Zhongcai Pei, Xingming Wu, Zhong Liu, Weihai Chen, Zhengguo Li

To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty.

Monocular Depth Estimation regression

A geometry-aware deep network for depth estimation in monocular endoscopy

1 code implementation20 Apr 2023 Yongming Yang, Shuwei Shao, Tao Yang, Peng Wang, Zhuo Yang, Chengdong Wu, Hao liu

To address this issue, we introduce a gradient loss to penalize edge fluctuations ambiguous around stepped edge structures and a normal loss to explicitly express the sensitivity to frequently small structures, and propose a geometric consistency loss to spreads the spatial information across the sample grids to constrain the global geometric anatomy structures.

3D Reconstruction Anatomy +1

Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset Refinement

1 code implementation20 Feb 2023 Zhong Liu, Ran Li, Shuwei Shao, Xingming Wu, Weihai Chen

In this work, we propose two novel ideas to improve self-supervised monocular depth estimation: 1) self-reference distillation and 2) disparity offset refinement.

Monocular Depth Estimation

SMUDLP: Self-Teaching Multi-Frame Unsupervised Endoscopic Depth Estimation with Learnable Patchmatch

no code implementations30 May 2022 Shuwei Shao, Zhongcai Pei, Weihai Chen, Xingming Wu, Zhong Liu, Zhengguo Li

Unsupervised monocular trained depth estimation models make use of adjacent frames as a supervisory signal during the training phase.

Depth Estimation

Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue

1 code implementation15 Dec 2021 Shuwei Shao, Zhongcai Pei, Weihai Chen, Wentao Zhu, Xingming Wu, Dianmin Sun, Baochang Zhang

Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios.

Depth Estimation Motion Estimation +1

Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than One

no code implementations16 Nov 2021 Shuwei Shao, Ran Li, Zhongcai Pei, Zhong Liu, Weihai Chen, Wentao Zhu, Xingming Wu, Baochang Zhang

In this work, we investigate into the phenomenon and propose to integrate the strengths of multiple weak depth predictor to build a comprehensive and accurate depth predictor, which is critical for many real-world applications, e. g., 3D reconstruction.

3D Reconstruction Ensemble Learning +2

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