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IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis
(2024)2023 IEEE/CVF International Conference on Computer Vision (ICCV)Existing inverse rendering combined with neural rendering methods can only perform editable novel view synthesis on object-specific scenes, while we present intrinsic neural radiance fields, dubbed IntrinsicNeRF, which introduce intrinsic decomposition into the NeRF-based neural rendering method and can extend its application to room-scale scenes. Since intrinsic decomposition is a fundamentally under-constrained inverse problem, we propose ...Conference Paper -
Intrinsicnerf: Learning intrinsic neural radiance fields for editable novel view synthesis
(2023)2023 IEEE/CVF International Conference on Computer Vision (ICCV)Existing inverse rendering combined with neural rendering methods can only perform editable novel view synthesis on object-specific scenes, while we present intrinsic neural radiance fields, dubbed IntrinsicNeRF, which introduce intrinsic decomposition into the NeRF-based neural rendering method and can extend its application to room-scale scenes. Since intrinsic decomposition is a fundamentally under-constrained inverse problem, we propose ...Conference Paper -
NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over-smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainly due to their simple fully-connected network architecture that does not incorporate local information in the observations. ...Conference Paper -
CompNVS: Novel View Synthesis with Scene Completion
(2022)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2022We introduce a scalable framework for novel view synthesis from RGB-D images with largely incomplete scene coverage. While generative neural approaches have demonstrated spectacular results on 2D images, they have not yet achieved similar photorealistic results in combination with scene completion where a spatial 3D scene understanding is essential. To this end, we propose a generative pipeline performing on a sparse grid-based neural ...Conference Paper -
LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies
(2021)2021 International Conference on 3D Vision (3DV)3D representation and reconstruction of human bodies have been studied for a long time in computer vision. Traditional methods rely mostly on parametric statistical linear models, limiting the space of possible bodies to linear combinations. It is only recently that some approaches try to leverage neural implicit representations for human body modeling, and while demonstrating impressive results, they are either limited by representation ...Conference Paper -
Holistic 3D Scene Understanding From a Single Image With Implicit Representation
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We present a new pipeline for holistic 3D scene understanding from a single image, which could predict object shapes, object poses, and scene layout. As it is a highly ill-posed problem, existing methods usually suffer from inaccurate estimation of both shapes and layout especially for the cluttered scene due to the heavy occlusion between objects. We propose to utilize the latest deep implicit representation to solve this challenge. We ...Conference Paper -
Towards Efficient Graph Convolutional Networks for Point Cloud Handling
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)We aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds. The basic graph convolution that is composed of a K-nearest neighbor (KNN) search and a multilayer perceptron (MLP) is examined. By mathematically analyzing the operations there, two findings to improve the efficiency of GCNs are obtained. (1) The local geometric structure information of 3D representations propagates ...Conference Paper -
Sat2Vid: Street-View Panoramic Video Synthesis From a Single Satellite Image
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)We present a novel method for synthesizing both temporally and geometrically consistent street-view panoramic video from a single satellite image and camera trajectory. Existing cross-view synthesis approaches focus on images, while video synthesis in such a case has not yet received enough attention. For geometrical and temporal consistency, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense ...Conference Paper -
OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems
(2020)2020 IEEE International Conference on Robotics and Automation (ICRA)In this paper, we present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras, which has a 360° coverage of stereo observations of the environment. For more practical and accurate reconstruction, we first introduce improved and light-weighted deep neural networks for the omnidirectional depth estimation, which are faster and more accurate ...Conference Paper -
Deep Shutter Unrolling Network
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We present a novel network for rolling shutter effect correction. Our network takes two consecutive rolling shutter images and estimates the corresponding global shutter image of the latest frame. The dense displacement field from a rolling shutter image to its corresponding global shutter image is estimated via a motion estimation network. The learned feature representation of a rolling shutter image is then warped, via the displacement ...Conference Paper