Leveraging Geometric Prior Uncertainty and Complementary Constraints for High-Fidelity Neural Indoor Surface Reconstruction
Department of Automation, Shanghai Jiao Tong University（自动化系，上海交通大学）；Department of Mechanical and Automation Engineering and T Stone Robotics Institute, The Chinese University of Hong Kong（机械与自动化工程系及T Stone机器人研究所，香港中文大学）
Qiyu Feng, Jiwei Shan, Shing Shin Cheng, Hesheng Wang
Neural implicit surface reconstruction with signed distance function has made significant progress, but recovering fine details such as thin structures and complex geometries remains challenging due to unreliable or noisy geometric priors. Existing approaches rely on implicit uncertainty that arises during optimization to filter these priors, which is indirect and inefficient, and masking supervision in high-uncertainty regions further leads to under-constrained optimization. To address these issues, we propose GPU-SDF, a neural implicit framework for indoor surface reconstruction that leverages geometric prior uncertainty and complementary constraints. We introduce a self-supervised module that explicitly estimates prior uncertainty without auxiliary networks. Based on this estimation, we design an uncertainty-guided loss that modulates prior influence rather than discarding it, thereby retaining weak but informative cues. To address regions with high prior uncertainty, GPU-SDF further incorporates two complementary constraints: an edge distance field that strengthens boundary supervision and a multi-view consistency regularization that enforces geometric coherence. Extensive experiments confirm that GPU-SDF improves the reconstruction of fine details and serves as a plug-and-play enhancement for existing frameworks. Source code will be available at https://github.com/IRMVLab/GPU-SDF
CUHK-Shenzhen（香港中文大学（深圳））；The Future Network of Intelligence Institute, CUHK-Shenzhen（香港中文大学（深圳）未来智联网络研究院）
We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images. Existing methods can only produce 3D shapes of indoor objects with limited geometry quality because of the heavy occlusion of indoor scenes. To solve this, we propose an instance-aligned implicit function (InstPIFu) for detailed object reconstruction. Combining with instance-aligned attention module, our method is empowered to decouple mixed local features toward the occluded instances. Additionally, unlike previous methods that simply represents the ro…
Beijing Jiaotong University（北京交通大学）；Institute of Information Science（信息科学研究所）；Beijing Key Laboratory of Advanced Information Science and Network Technology（北京先进信息科学与网络技术重点实验室）
Recently, horizontal representation-based panoramic semantic segmentation approaches outperform projection-based solutions, because the distortions can be effectively removed by compressing the spherical data in the vertical direction. However, these methods ignore the distortion distribution prior and are limited to unbalanced receptive fields, e.g., the receptive fields are sufficient in the vertical direction and insufficient in the horizontal direction. Differently, a vertical representation compressed in another direction can offer implicit distortion prior and enlarge horizontal receptiv…
CustomTex: High-fidelity Indoor Scene Texturing via Multi-Reference Customization
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）
Many research works focus on leveraging the complementary geometric information of indoor depth sensors in vision tasks performed by deep convolutional neural networks, notably semantic segmentation. These works deal with a specific vision task known as "RGB-D Indoor Semantic Segmentation". The challenges and resulting solutions of this task differ from its standard RGB counterpart. This results in a new active research topic. The objective of this paper is to introduce the field of Deep Convolutional Neural Networks for RGB-D Indoor Semantic Segmentation. This review presents the most popular…
RoomEditor++: A Parameter-Sharing Diffusion Architecture for High-Fidelity Furniture Synthesis
School of Artificial Intelligence, Tianjin University（天津大学人工智能学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Engineering Research Center of City Intelligence and Digital Governance, Ministry of Education（教育部城市智能与数字治理工程研究中心）
Recovery of an underlying scene geometry from multiview images stands as a long-time challenge in computer vision research. The recent promise leverages neural implicit surface learning and differentiable volume rendering, and achieves both the recovery of scene geometry and synthesis of novel views, where deep priors of neural models are used as an inductive smoothness bias. While promising for object-level surfaces, these methods suffer when coping with complex scene surfaces. In the meanwhile, traditional multi-view stereo can recover the geometry of scenes with rich textures, by globally o…
VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis
Technical University of Munich（慕尼黑工业大学）；Huawei Munich Research Center（华为慕尼黑研究中心）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Stuttgart（斯图加特大学）；Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用技术大学）
Dual-Latent Collaborative Decoding for Fidelity-Perception Balanced Image Compression
School of Information and Communication Engineering and the State Key Laboratory of Media Convergence and Communication, Communication University of China（信息与通信工程学院和媒体融合与通信国家重点实验室，中国通信大学）；School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（信息科学与电子工程学院，上海交通大学）；Department of Computer Science, City University of Hong Kong（计算机科学系，香港城市大学）；State Key Laboratory of Multimedia information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机科学学院，北京大学）
EAG-PT: Emission-Aware Gaussians and Path Tracing for Diffuse Indoor Scene Reconstruction and Editing
Zhejiang University（浙江大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）
MIRAGE: Metric Inference and Reliability-Aware Gaussian Estimation from Feed-Forward Vision
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The University of Hong Kong（香港大学）；University of Science and Technology of China（中国科学技术大学）；Fudan University（复旦大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）
ARTDECO: Towards Efficient and High-Fidelity On-the-Fly 3D Reconstruction with Structured Scene Representation
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Jiao Tong University（上海交通大学）；The Chinese University of Hong Kong（香港中文大学）；Carnegie Mellon University（卡内基梅隆大学）；Zhejiang University（浙江大学）；The University of Hong Kong（香港大学）
VoxScene: Anchor-Conditioned Voxel Diffusion for Indoor Scene Arrangement
Shanghai Jiao Tong University（上海交通大学）；Hong Kong University of Science and Technology（香港科技大学）；Tsinghua University（清华大学）；University of Cambridge（剑桥大学）；Peking University（北京大学）
HaloGS: Loose Coupling of Compact Geometry and Gaussian Splats for 3D Scenes
Zhejiang University（浙江大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；The Chinese University of Hong Kong（香港中文大学）；Inria（法国国家信息与自动化技术研究院）；The University of Hong Kong（香港大学）
CasLayout: Cascaded 3D Layout Diffusion for Indoor Scene Synthesis with Implicit Relation Modeling
MAIS, Institute of Automation, Chinese Academy of Sciences and School of Artificial Intelligence, University of Chinese Academy of Sciences（MAIS，自动化研究所，中国科学院，人工智能学院，中国科学院大学）；Tsinghua University（清华大学）；SKLMS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（系统科学研究所，中国科学院）；Microsoft Research Asia（微软亚洲研究院）
CASA-SDF: Curriculum-Aware Spatial Adaptation with Curvature-Guided Density for Neural Implicit Surface Reconstruction
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）
Principled Feature Disentanglement for High-Fidelity Unified Brain MRI Synthesis
School of Computing, Korea Advanced Institute of Science（韩国科学技术院计算机学院）；Samsung Research, Seoul 06765, Republic of Korea（三星研究院）；Gordon Center for Medical Imaging, Massachusetts General Hospital（麻省总医院影像中心）；Harvard Medical School, Boston, MA 02114, USA（哈佛医学院）
NeVStereo: A NeRF-Driven NVS-Stereo Architecture for High-Fidelity 3D Tasks
University of Washington（华盛顿大学）；University of Southern California（南加州大学）；CUHK-Shenzhen（香港中文大学（深圳））；USC Institute for Creative Technologies（南加州大学创意技术研究所）
