Author name cluster

You Shen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

AAAI Conference 2026 Conference Paper

DeOcc-1-to-3: 3D De-Occlusion from a Single Image via Self-Supervised Multi-View Diffusion

Yansong Qu
Shaohui Dai
Xinyang Li
Yuze Wang
You Shen
Shengchuan Zhang
Liujuan Cao

Reconstructing 3D objects from a single image is a long-standing challenge, particularly under real-world occlusions. While recent diffusion-based view synthesis models can generate consistent novel views from a single RGB image, they generally assume fully visible inputs and struggle when parts of the object are occluded, leading to inconsistent views and degraded 3D reconstruction quality. To address this limitation, we propose DeOcc-1-to-3, an end-to-end framework for occlusion-aware multi-view generation. Our method directly synthesizes six structurally consistent novel views from a single partially occluded image, enabling downstream 3D reconstruction without requiring prior inpainting or manual annotations. We design a self-supervised training pipeline that leverages occluded–unoccluded image pairs and pseudo-ground-truth views to guide structure-aware completion and view consistency. Without modifying the original architecture, we fully fine-tune the diffusion model to jointly learn completion and multi-view generation. Additionally, we introduce the first benchmark for occlusion-aware reconstruction, covering diverse occlusion levels, object categories, and mask patterns, providing a standardized evaluation protocol.

PDF Details DOI

AAAI Conference 2026 Conference Paper

S²Teacher: Step-by-step Teacher for Sparsely Annotated Oriented Object Detection

Yu Lin
Jianghang Lin
Kai Ye
You Shen
Shengchuan Zhang
Liujuan Cao

Although fully-supervised oriented object detection has made significant progress in remote sensing image understanding, it comes at the cost of labor-intensive annotation. Recent studies have explored weakly and semi-supervised learning to alleviate this burden. However, these methods overlook the difficulties posed by dense annotations in complex remote sensing scenes. In this paper, we introduce a novel setting called sparsely annotated oriented object detection (SAOOD), which only labels partial instances, and propose a solution to address its challenges. Specifically, we focus on two key issues in the setting: (1) sparse labeling leading to overfitting on limited foreground representations, and (2) unlabeled objects (false negatives) confusing feature learning. To this end, we propose the S2Teacher, a novel angle-consistency guided method that progressively mines pseudo-labels for unlabeled objects from easy to hard, enhancing foreground representations. Additionally, it reweights the loss of unlabeled objects to mitigate their impact during training. Extensive experiments demonstrate that S2Teacher not only significantly improves detector performance across different sparse annotation levels but also achieves near-fully-supervised performance on the DOTA dataset with only 10% annotation instances, effectively balancing accuracy and labeling cost.

PDF Details DOI

ICRA Conference 2025 Conference Paper

MDC-Seg: Multi-Directional Convolution-Based Semantic Segmentation for LiDAR Point Clouds

Xin Ouyang
Xiaolong Qian
Yunzhou Zhang
You Shen
Guiyuan Wang
Wei Liu 0022

LiDAR point clouds 3D semantic segmentation enables efficient and accurate environmental sensing for intelligent vehicles and autonomous robots, greatly advancing these domains. Existing advanced methods that use 3D sparse convolutional often suffer from a small Effective Receptive Field (ERF), which limits context sensing and challenging highperformance segmentation. Building on this observation, we propose MDC-Seg for efficient ERF enlargement. We design Multi-directional Convolution (MDConv), which simultaneously performs sparse feature encoding on the Bird's Eye View (BEV) and Range View (RV) planes to enlarge the ERF of 3D sparse convolution. To enhance feature fusion in MDConv, we introduce an attention mechanism and design an efficient multifeature fusion (EMFF) module suitable for both 3D and 2D sparse features. To improve segmentation accuracy, we design a point-voxel constraint (PVC) module to handle edge voxels containing multiple point cloud categories, optimizing the final inference results. These modules add minimal memory and inference time but significantly improve performance compared to the baseline. Extensive experiments on the SemanticKITTI benchmark demonstrate MDC-Seg's excellent performance, with supplementary tests on nuScenes further confirming its superiority by yielding good results. The source code is available at https://github.com/OYgreat-river/MDC-Seg.

Details

IROS Conference 2024 Conference Paper

HSS-SLAM: Human-in-the-Loop Semantic SLAM Represented by Superquadrics

Yulong Li
Yunzhou Zhang
Bin Zhao
Zhiyao Zhang
You Shen
Tengda Zhang
Guolu Chen

The advancement of object detection algorithms has catalyzed the development of object-level semantic SLAM. However, due to missed and false detections, object-level semantic SLAM fails to represent the objects within the scene adequately. Therefore, this paper proposes a novel object-level semantic SLAM termed HSS-SLAM. We incorporate human-in-the-loop into our method, establishing an interaction module to facilitate human editing and rectifying semantic information. Additionally, to minimize the manual correction workload, a lightweight and intuitive method for semantic extension is proposed, augmenting the semantic richness of the global map with a few operations. Furthermore, our method adopts superquadrics for object representation, enabling detailed descriptions of various object shapes. This mitigates the limitation of conventional semantic mapping, where objects are difficult to distinguish due to the reliance on a single-shape representation. Subsequently, precise estimation of superquadric parameters and camera poses is achieved through joint optimization. Extensive experiments conducted on TUM RGB-D and Scenes V2 datasets demonstrate that the proposed approach exhibits competitive performance, surpassing current methods in both object representation and camera localization accuracy.

Details

ICRA Conference 2024 Conference Paper

VPE-SLAM: Neural Implicit Voxel-permutohedral Encoding for SLAM

Zhiyao Zhang
Yunzhou Zhang
You Shen
Lei Rong
Sizhan Wang
Xin Ouyang
Yulong Li

NeRF can reconstruct incredibly realistic environmental maps in dense simultaneous localization and mapping, providing robots with more comprehensive scene map information. However, NeRF often struggles with geometric distortions in indoor reconstructions. To correct geometric distortions, we develop VPE-SLAM, based on the proposed voxel-permutohedral encoding, which can incrementally reconstruct maps of unknown scenes. Specifically, voxel-permutohedral encoding combines a sparse voxel feature grid created by an octree and multi-resolution permutohedral tetrahedral feature grids to represent the scene effectively. Especially when dealing with object edges, our method can effectively encode the geometry and texture of edges by the hybrid structural grid. We propose a novel local bundle adjustment module that utilizes a sliding window mechanism to manage adjacent keyframes requiring optimization. Furthermore, the proposed method establishes local map consistency by repeatedly optimizing keyframes that were initially under-optimized through a compensation strategy. The consistency of the local map can enhance the adaptability of our method to challenging scenes. Extensive experiments demonstrate that our method can achieve accurate camera tracking and produce high-quality reconstruction results on the Replica and ScanNet datasets. The source code will be available at https://github.com/NeuCV-IRMI/VPE-SLAM.

Details

IROS Conference 2023 Conference Paper

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

You Shen
Yunzhou Zhang
Yanmin Wu
Zhenyu Wang 0010
Linghao Yang
Sonya Coleman
Dermot Kerr

The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception. Existing methods estimate specific 3D shapes and achieve remarkable performance. However, these methods rely on extensive computation and memory, causing imbalances between accuracy and real-time performance. To tackle this challenge, we propose a novel LiDAR-based 3D object detection model named BSH-Det3D, which applies an effective way to enhance spatial features by estimating complete shapes from a bird's eye view (BEV). Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes. The PSC module generates a BEV shape heatmap for each scene. After integrating with heatmaps, BSH-Det3D can provide additional information in shape deterioration areas and generate high-quality 3D proposals. We also design an attention-based densification fusion module (ADF) to adaptively associate the sparse features with heatmaps and raw points. The ADF module integrates the advantages of points and shapes knowledge with negligible overheads. Extensive experiments on the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D. The source code is available on https://github.com/mystorm16/BSH-Det3D.

Details