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学术成果与研究动态

学术论文

Structure-aware Indoor Scene Reconstruction via Two Levels of Abstraction

2021
ISPRS
三维重建

In this paper, we propose a novel approach that reconstructs the indoor scene in a structure-aware manner and produces two meshes with different levels of abstraction. To be precise, we start from the raw triangular mesh of indoor scene and decompose it into two parts: structure and non-structure objects. On the one hand, structure objects are defined as significant permanent parts in the indoor environment such as floors, ceilings and walls. In the proposed algorithm, structure objects are abstracted by planar primitives and assembled into a polygonal structure mesh. This step produces a compact structure-aware watertight model that decreases the complexity of original mesh by three orders of magnitude. On the other hand, non-structure objects are movable objects in the indoor environment such as furniture and interior decoration. Meshes of these objects are repaired and simplified according to their relationship with respect to structure primitives.

Floorplan Generation from 3D Point Clouds: A Space Partitioning Approach

2021
ISPRS
场景理解、户型图

We propose a novel approach to automatically reconstruct the foorplan of indoor environments from raw sensor data. In contrast to existing methods that generate foorplans under the form of a planar graph by detecting corner points and connecting them, our framework employs a strategy that decomposes the space into a polygonal partition and selects edges that belong to wall structures by energy minimization. By relying on a efficient space-partitioning data structure instead of a traditional and delicate corner detection task, our framework offers a high robustness to imperfect data. We demonstrate the potential of our algorithm on both RGBD and LIDAR points scanned from simple to complex scenes. Experimental results indicate that our method is competitive with respect to existing methods in terms of geometric accuracy and output simplicity.

PhyIR: Physics-Based Inverse Rendering for Panoramic Indoor Images

2022
CVPR
MR、逆渲染、场景理解

Inverse rendering of complex material such as glossy, metal and mirror material is a long-standing ill-posed problem in this area, which has not been well solved. Previous approaches cannot tackle them well due to simplified BRDF and unsuitable illumination representations. In this paper, we present PhyIR, a neural inverse rendering method with a more completed SVBRDF representation and a physics-based in-network rendering layer, which can handle complex material and incorporate physical constraints by re-rendering realistic and detailed specular reflectance. Our framework estimates geometry, material and Spatially-Coherent (SC) illumination from a single indoor panorama. Due to the lack of panoramic datasets with completed SVBRDF and full-spherical light probes, we introduce an artist-designed dataset named FutureHouse with high-quality geometry, SVBRDF and per-pixel Spatially-Varying (SV) lighting. To ensure the coherence of SV lighting, a novel SC loss is proposed.

Multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes

2023
CVPR
逆渲染、三维重建、场景理解

We present a multi-view inverse rendering method for large-scale real-world indoor scenes that reconstructs global illumination and physically-reasonable SVBRDFs. Unlike previous representations, where the global illumination of large scenes is simplified as multiple environment maps, we propose a compact representation called Texture-based Lighting (TBL). It consists of 3D meshs and HDR textures, and efficiently models direct and infinite-bounce indirect lighting of the entire large scene. Based on TBL, we further propose a hybrid lighting representation with precomputed irradiance, which significantly improves the efficiency and alleviate the rendering noise in the material optimization. To physically disentangle the ambiguity between materials, we propose a three-stage material optimization strategy based on the priors of semantic segmentation and room segmentation. Extensive experiments show that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively

技术解读

3D物品检测算法及应用

在图像上进行2D物品检测，在学术界已有较多的研究，在工业界也有成熟的应用。但是物理空间实际是3D的，在与空间数字化紧密相连的一些应用中，业务往往还需要关注物品的3D位置。针对数据获取成本和数据处理难度问题，如视都进行了研究并积累了较多成果。如视采集了千万量级套数的室内场景数据，利用Galois激光扫描采集点云，或大数据训练的智能算法估算深度，大量的数据为3D检测提供了坚实的基础。在3D检测算法方面，研究、实现多种技术路线，针对不同的需求。

混合现实解读

虚拟现实（MR、 Mixed Reality）在沉浸式真实感混合现实领域发挥重要作用，它通过计算机技术将虚拟的信息叠加到现实中，实现内容、空间几何、时间、光照等多个层面的融合，提升人的感官体验。虚拟物品合成需要做到几何和光照一致性，几何一致性用以保证虚拟物体与真实世界保持空间几何的一致，符合正确的透过关系、遮挡关系，光照一致性要求虚拟物品具有与真实场景相似的光照环境，产生正确的着色效果，并在真实场景中投下符合光照的阴影。

如视单目深度预测算法

在如视的各种信息采集设备中, 全景相机 / 不带lidar的手机 / 云台等的深度是利用深度学习算法, 从彩色图直接推测出来的. 这种以单张彩色图为输入, 预测对应点的深度的算法, 称之为单目深度预测算法.

学术动态

世界领先！如视研究成果入选CVPR 2023，室内多视角逆渲染技术重磅革新

如视研究成果入选CVPR 2022，逆渲染实现对真实世界更深层数字化

如视荣获2021“直通乌镇”全球互联网大赛消费互联网专题赛一等奖

如视惠新宸：面对居住服务痛点以科技改善用户体验

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