Search Results for author: Kaichen Zhou

Found 18 papers, 7 papers with code

SCANet: Correcting LEGO Assembly Errors with Self-Correct Assembly Network

1 code implementation27 Mar 2024 Yuxuan Wan, Kaichen Zhou, jinhong Chen, Hao Dong

To support research in this area, we present the LEGO Error Correction Assembly Dataset (LEGO-ECA), comprising manual images for assembly steps and instances of assembly failures.

WSCLoc: Weakly-Supervised Sparse-View Camera Relocalization

no code implementations22 Mar 2024 Jialu Wang, Kaichen Zhou, Andrew Markham, Niki Trigoni

Despite the advancements in deep learning for camera relocalization tasks, obtaining ground truth pose labels required for the training process remains a costly endeavor.

Camera Relocalization Image Reconstruction +1

OV-NeRF: Open-vocabulary Neural Radiance Fields with Vision and Language Foundation Models for 3D Semantic Understanding

no code implementations7 Feb 2024 Guibiao Liao, Kaichen Zhou, Zhenyu Bao, Kanglin Liu, Qing Li

First, from the single-view perspective, we introduce Region Semantic Ranking (RSR) regularization by leveraging 2D mask proposals derived from SAM to rectify the noisy semantics of each training view, facilitating accurate semantic field learning.

SERF: Fine-Grained Interactive 3D Segmentation and Editing with Radiance Fields

no code implementations26 Dec 2023 Kaichen Zhou, Lanqing Hong, Enze Xie, Yongxin Yang, Zhenguo Li, Wei zhang

Although significant progress has been made in the field of 2D-based interactive editing, fine-grained 3D-based interactive editing remains relatively unexplored.

Interactive Segmentation Segmentation

MGDepth: Motion-Guided Cost Volume For Self-Supervised Monocular Depth In Dynamic Scenarios

no code implementations23 Dec 2023 Kaichen Zhou, Jia-Xing Zhong, Jia-Wang Bian, Qian Xie, Jian-Qing Zheng, Niki Trigoni, Andrew Markham

Despite advancements in self-supervised monocular depth estimation, challenges persist in dynamic scenarios due to the dependence on assumptions about a static world.

Computational Efficiency Monocular Depth Estimation +1

Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation

no code implementations18 Dec 2023 Sangyun Shin, Kaichen Zhou, Madhu Vankadari, Andrew Markham, Niki Trigoni

Coarse-to-fine 3D instance segmentation methods show weak performances compared to recent Grouping-based, Kernel-based and Transformer-based methods.

3D Instance Segmentation Semantic Segmentation

DynPoint: Dynamic Neural Point For View Synthesis

1 code implementation NeurIPS 2023 Kaichen Zhou, Jia-Xing Zhong, Sangyun Shin, Kai Lu, Yiyuan Yang, Andrew Markham, Niki Trigoni

The introduction of neural radiance fields has greatly improved the effectiveness of view synthesis for monocular videos.

ConsistentNeRF: Enhancing Neural Radiance Fields with 3D Consistency for Sparse View Synthesis

1 code implementation18 May 2023 Shoukang Hu, Kaichen Zhou, Kaiyu Li, Longhui Yu, Lanqing Hong, Tianyang Hu, Zhenguo Li, Gim Hee Lee, Ziwei Liu

In this paper, we propose ConsistentNeRF, a method that leverages depth information to regularize both multi-view and single-view 3D consistency among pixels.

3D Reconstruction SSIM

Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR

no code implementations21 Sep 2022 Sangyun Shin, Stuart Golodetz, Madhu Vankadari, Kaichen Zhou, Andrew Markham, Niki Trigoni

Supervised approaches typically require the annotation of large training sets; there has thus been great interest in leveraging weakly, semi- or self-supervised methods to avoid this, with much success.

3D Object Detection Object +2

DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction

1 code implementation14 Sep 2022 Kaichen Zhou, Lanqing Hong, Changhao Chen, Hang Xu, Chaoqiang Ye, Qingyong Hu, Zhenguo Li

Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames.

Depth Estimation

DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning

no code implementations1 Jan 2021 Kaichen Zhou, Lanqing Hong, Fengwei Zhou, Binxin Ru, Zhenguo Li, Trigoni Niki, Jiashi Feng

Our method performs co-optimization of the neural architectures, training hyper-parameters and data augmentation policies in an end-to-end fashion without the need of model retraining.

BIG-bench Machine Learning Computational Efficiency +2

Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling

no code implementations26 Jun 2020 Chengliang Dai, Shuo Wang, Yuanhan Mo, Kaichen Zhou, Elsa Angelini, Yike Guo, Wenjia Bai

Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks.

BIG-bench Machine Learning Image Segmentation +2

VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization

1 code implementation12 Mar 2020 Kaichen Zhou, Changhao Chen, Bing Wang, Muhamad Risqi U. Saputra, Niki Trigoni, Andrew Markham

We conjecture that this is because of the naive approaches to feature space fusion through summation or concatenation which do not take into account the different strengths of each modality.

Camera Relocalization Visual Localization

Tighter Bound Estimation of Sensitivity Analysis for Incremental and Decremental Data Modification

no code implementations6 Mar 2020 Kaichen Zhou, Shiji Song, Gao Huang, Wu Cheng, Quan Zhou

Specifically, the proposed algorithm can be used to estimate the upper and lower bounds of the updated classifier's coefficient matrix with a low computational complexity related to the size of the updated dataset.

Incremental Learning L2 Regularization

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