Lightweight Multiplane Images Network for Real-Time Stereoscopic Conversion from Planar Video

• URL: http://arxiv.org/abs/2412.03102v1
• Date: Wed, 04 Dec 2024 08:04:14 GMT
• Title: Lightweight Multiplane Images Network for Real-Time Stereoscopic Conversion from Planar Video
• Authors: Shanding Diao, Yang Zhao, Yuan Chen, Zhao Zhang, Wei Jia, Ronggang Wang,
• Abstract summary: This paper proposes a real-time stereoscopic conversion network based on multi-plane images (MPI) It employs a lightweight depth-semantic branch to extract depth-aware features implicitly. It can achieve comparable performance to some state-of-the-art (SOTA) models and support real-time inference at 2K resolution.
• Score: 29.199113565852645
• License:
• Abstract: With the rapid development of stereoscopic display technologies, especially glasses-free 3D screens, and virtual reality devices, stereoscopic conversion has become an important task to address the lack of high-quality stereoscopic image and video resources. Current stereoscopic conversion algorithms typically struggle to balance reconstruction performance and inference efficiency. This paper proposes a planar video real-time stereoscopic conversion network based on multi-plane images (MPI), which consists of a detail branch for generating MPI and a depth-semantic branch for perceiving depth information. Unlike models that depend on explicit depth map inputs, the proposed method employs a lightweight depth-semantic branch to extract depth-aware features implicitly. To optimize the lightweight branch, a heavy training but light inference strategy is adopted, which involves designing a coarse-to-fine auxiliary branch that is only used during the training stage. In addition, the proposed method simplifies the MPI rendering process for stereoscopic conversion scenarios to further accelerate the inference. Experimental results demonstrate that the proposed method can achieve comparable performance to some state-of-the-art (SOTA) models and support real-time inference at 2K resolution. Compared to the SOTA TMPI algorithm, the proposed method obtains similar subjective quality while achieving over $40\times$ inference acceleration.

Related papers

• Pixel-Aligned Multi-View Generation with Depth Guided Decoder [86.1813201212539]
  We propose a novel method for pixel-level image-to-multi-view generation. Unlike prior work, we incorporate attention layers across multi-view images in the VAE decoder of a latent video diffusion model. Our model enables better pixel alignment across multi-view images.
  arXiv  Detail & Related papers  (2024-08-26T04:56:41Z)
• OrientDream: Streamlining Text-to-3D Generation with Explicit Orientation Control [66.03885917320189]
  OrientDream is a camera orientation conditioned framework for efficient and multi-view consistent 3D generation from textual prompts. Our strategy emphasizes the implementation of an explicit camera orientation conditioned feature in the pre-training of a 2D text-to-image diffusion module. Our experiments reveal that our method not only produces high-quality NeRF models with consistent multi-view properties but also achieves an optimization speed significantly greater than existing methods.
  arXiv  Detail & Related papers  (2024-06-14T13:16:18Z)
• GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision [49.839374549646884]
  This paper presents GEOcc, a Geometric-Enhanced Occupancy network tailored for vision-only surround-view perception. Our approach achieves State-Of-The-Art performance on the Occ3D-nuScenes dataset with the least image resolution needed and the most weightless image backbone.
  arXiv  Detail & Related papers  (2024-05-17T07:31:20Z)
• Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring [25.36888929483233]
  We propose a multi-scale network based on single-input and multiple-outputs(SIMO) for motion deblurring. We combine the characteristics of real-world trajectories with a learnable wavelet transform module to focus on the directional continuity and frequency features of the step-by-step transitions between blurred images to sharp images.
  arXiv  Detail & Related papers  (2023-12-29T02:59:40Z)
• Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring [113.96237446327795]
  We present a novel self-supervised event-guided deep hierarchical Multi-patch Network to deal with blurry images and videos. We also propose an event-guided architecture to exploit motion cues contained in videos to tackle complex blur in videos. Our MPN achieves the state of the art on the GoPro and VideoDeblurring datasets with a 40x faster runtime compared to current multi-scale methods.
  arXiv  Detail & Related papers  (2023-02-14T15:58:00Z)
• Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation [18.14237514372724]
  We propose a new framework to generate 3D human pose and mesh from RGB videos. We train a two-stream temporal network based on transformer to predict SMPL parameters. The proposed algorithm is extensively evaluated on the Human3.6 and 3DPW datasets.
  arXiv  Detail & Related papers  (2021-10-22T10:01:13Z)
• Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo [103.08512487830669]
  We present a modern solution to the multi-view photometric stereo problem (MVPS) We procure the surface orientation using a photometric stereo (PS) image formation model and blend it with a multi-view neural radiance field representation to recover the object's surface geometry. Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.
  arXiv  Detail & Related papers  (2021-10-11T20:20:03Z)
• Real-time Dense Reconstruction of Tissue Surface from Stereo Optical Video [10.181846237133167]
  We propose an approach to reconstruct dense three-dimensional (3D) model of tissue surface from stereo optical videos in real-time. The basic idea is to first extract 3D information from video frames by using stereo matching, and then to mosaic the reconstructed 3D models. Experimental results on ex- and in vivo data showed that the reconstructed 3D models have high resolution texture with an accuracy error of less than 2 mm.
  arXiv  Detail & Related papers  (2020-07-16T19:14:05Z)
• Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation [57.11299763566534]
  We present a solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. We exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections.
  arXiv  Detail & Related papers  (2020-04-05T12:52:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.