Solutions
AI Agent Understanding

Indoor smart devices such as robotic vacuum cleaners, home care robots, indoor drones, etc., need to cope with complex indoor environments, and possess the ability for environmental mapping, navigation planning, and object recognition. Coohom Cloud aims to assist smart device manufacturers in cost-effective data collection and utilization through large-scale indoor environmental data, including annotated 2D image datasets and 3D environmental data.

AIGC

In the era of AIGC technology explosion, various generative large-scale models have emerged. Typically, these models are pre-trained based on a large amount of data. Coohom Cloud can provide training data resources including images, videos, models, and other elements, along with a comprehensive labeling system, to assist AIGC researchers in achieving breakthroughs in large-scale model technology.

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Robotic Simulation

Robot simulation allows for quick, cost-effective, and safe validation of products. Coohom Cloud provides a simulation environment with high rendering quality and physical realism, combined with specific simulation platforms to create a simulation environment database, offering support for enterprise robot simulations.

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Visualized Product Promotion

The visualized product promotion is to empower customers to experience product charm. Coohom Cloud, through its 3D data resource library and high-fidelity environmental display capabilities, immerse users to in the allure of products within a virtual environment.

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XR

High-quality datasets significantly enhance the environmental adaptation and user experience of AR/VR/MR systems, such as precise virtual-physical integration effects and smooth interactive operations. Simultaneously, datasets empower content creators to access necessary 3D models, textures, and animation resources, driving content innovation and thus effectively advancing the overall progress and widespread application of XR technology.

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The Future Begins Here

Here gathers the research findings and technologies in the fields of synthetic data, AIGC, intelligent agents, etc. from the Coohomcloud team.
2022

SIGGRAPH Asia 2022

Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing

Jingsen Zhu, Fujun Luan, Yuchi Huo, Zihao Lin, Zhihua Zhong, Dianbing Xi Rui Wang, Hujun Bao, Jiaxiang Zheng, Tang Rui
Abstract: Indoor scenes typically exhibit complex, spatially-varying appearance from global illumination, making inverse rendering a challenging ill-posed problem. This work presents an end-to-end, learning-based inverse rendering framework incorporating differentiable Monte Carlo raytracing with importance sampling. The framework takes a single image as input to jointly recover the underlying geometry, spatially-varying lighting, and...
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2022

CGF 2022

MINERVAS: Massive Interior Environments Virtual Synthesis

Haocheng Ren, Hao Zhang,Jia Zheng, Jiaxiang Zheng,Rui Tang, Yuchi Huo,Hujun Bao,Rui Wang
Abstract: This paper presents MINERVAS, a Massive INterior EnviRonments VirtuAl Synthesis system, to facilitate the 3D scene modification and the 2D image synthesis for various vision tasks. In particular, we design a programmable pipeline with Domain-Specific Language, allowing users to (1) select scenes from the commercial indoor scene database, (2) synthesize scenes for different tasks with customized rules, and (3) render...
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2022

WACV 2022

Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image

Cheng Yang, Jia Zheng, Xili Dai, Rui Tang, Yi Ma, Xiaojun Yuan
Abstract: Single-image room layout reconstruction aims to reconstruct the enclosed 3D structure of a room from a single image. Most previous work relies on the cuboid-shape prior. This paper considers a more general indoor assumption, i.e., the room layout consists of a single ceiling, a single floor, and several vertical walls. To this end, we first ...
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2021

CVPR 2021

Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

Zhaoyuan Yin, Jia Zheng, Weixin Luo, Shenhan Qian, Hanling Zhang, Shenghua Gao
Abstract: This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively. Then, based on the user annotations, a segmentation algorithm refines the masks. The previous interactive VOS paradigm selects the frame with some worst evaluation metric, and...
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2021

CVPR 2021

Layout-Guided Novel View Synthesis from a Single Indoor Panorama

Jiale Xu, Jia Zheng, Yanyu Xu, Rui Tang, Shenghua Gao
Abstract: Existing view synthesis methods mainly focus on the perspective images and have shown promising results. However, due to the limited field-of-view of the pinhole camera, the performance quickly degrades when large camera movements are adopted. In this paper, we make the first attempt to generate novel views from a single indoor panorama and take the large camera translations into consideration. To tackle...
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2020

ECCV 2020

Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling

Jia Zheng, Junfei Zhang, Jing Li, Rui Tang, Shenghua Gao, Zihan Zhou
Abstract: Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding. However, the ground truth annotations are often obtained via human labor, which is particularly challenging and inefficient for such tasks due to the large number of 3D structure instances (e.g., line segments) and other factors such as viewpoints and occlusions. In this paper, we present a new synthetic dataset, Structured3D, with the aim of providing large-scale photo-realistic images with rich 3D structure annotations for a...
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2020

CVPR 2020

Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only

Qi Chen, Qi Wu, Rui Tang, Yuhan Wang, Shuai Wang, Mingkui Tan
Abstract: Home design is a complex task that normally requires architects to finish with their professional skills and tools. It will be fascinating that if one can produce a house plan intuitively without knowing much knowledge about home design and experience of using complex designing tools, for example, via natural language. In this paper, we formulate it as a language conditioned visual content generation problem that is further divided into a floor plan generation and an interior texture (such as floor and wall) synthesis task. The only control signal of the generation process is the linguistic expression given by...
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2020

CVPR 2020

Geometric Structure Based and Regularized Depth Estimation From 360 Indoor Imagery

Lei Jin, Yanyu Xu, Jia Zheng, Junfei Zhang, Rui Tang, Shugong Xu, Jingyi Yu, Shenghua Gao
Abstract: Motivated by the correlation between the depth and the geometric structure of a 360 indoor image, we propose a novel learning-based depth estimation framework that leverages the geometric structure of a scene to conduct depth estimation. Specifically, we represent the geometric structure of an indoor scene as a collection of corners, boundaries and planes. On the one hand, once a depth map is estimated, this ...
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2019

SIGGRAPH Asia 2019

Data-driven Interior Plan Generation for Residential Buildings

Wenming Wu, Xiao-Ming Fu, Rui Tang, Yuhan Wang, Yu-Hao Qi, Ligang Liu
Abstract: Home design is a complex task that normally requires architects to finish with their professional skills and tools. It will be fascinating that if one can produce a house plan intuitively without knowing much knowledge about home design and experience of using complex designing tools, for example, via natural language. In this paper, we formulate it as a language conditioned visual content generation problem that is further divided into a floor plan generation and an interior texture (such as floor and wall) synthesis task. The only control signal of the generation process is...
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2019

SIGGRAPH Asia 2019

Adversarial Monte Carlo Denoising with Conditioned Auxiliary Feature Modulation

Bing Xu,Junfei Zhang,Rui Wang,Kun Xu,Yong-Liang Yang,Chuan LI, Rui Tang
Abstract: Denoising Monte Carlo rendering with a very low sample rate remains a major challenge in the photo-realistic rendering research. Many previous works, including regression-based and learning-based methods, have been explored to achieve better rendering quality with less computational cost. However, most of these methods rely on handcrafted optimization objectives, which lead to artifacts such as blurs and unfaithful details. In this paper, we present an adversarial approach ...
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2018

BMVC 2018

InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset

Wenbin Li, Sajad Saeedi, John McCormac, Ronald Clark, Dimos Tzoumanikas, Qing Ye, Yuzhong Huang, Rui Tang, Stefan Leutenegger
Abstract: Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM). Without a doubt, synthetic imagery bears a vast potential due to scalability in terms of amounts of data obtainable without tedious manual ground truth annotations ...
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