Sun Off, Lights On: Photorealistic Monocular Nighttime Simulation for Robust Semantic Perception

• URL: http://arxiv.org/abs/2407.20336v1
• Date: Mon, 29 Jul 2024 18:00:09 GMT
• Title: Sun Off, Lights On: Photorealistic Monocular Nighttime Simulation for Robust Semantic Perception
• Authors: Konstantinos Tzevelekakis, Shutong Zhang, Luc Van Gool, Christos Sakaridis,
• Abstract summary: Nighttime scenes are hard to semantically perceive with learned models and annotate for humans. Our method, named Sun Off, Lights On (SOLO), is the first to perform nighttime simulation on single images in a photorealistic fashion by operating in 3D. Not only is the visual quality and photorealism of our nighttime images superior to competing approaches including diffusion models, but the former images are also proven more beneficial for semantic nighttime segmentation in day-to-night adaptation.
• Score: 53.631644875171595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nighttime scenes are hard to semantically perceive with learned models and annotate for humans. Thus, realistic synthetic nighttime data become all the more important for learning robust semantic perception at night, thanks to their accurate and cheap semantic annotations. However, existing data-driven or hand-crafted techniques for generating nighttime images from daytime counterparts suffer from poor realism. The reason is the complex interaction of highly spatially varying nighttime illumination, which differs drastically from its daytime counterpart, with objects of spatially varying materials in the scene, happening in 3D and being very hard to capture with such 2D approaches. The above 3D interaction and illumination shift have proven equally hard to model in the literature, as opposed to other conditions such as fog or rain. Our method, named Sun Off, Lights On (SOLO), is the first to perform nighttime simulation on single images in a photorealistic fashion by operating in 3D. It first explicitly estimates the 3D geometry, the materials and the locations of light sources of the scene from the input daytime image and relights the scene by probabilistically instantiating light sources in a way that accounts for their semantics and then running standard ray tracing. Not only is the visual quality and photorealism of our nighttime images superior to competing approaches including diffusion models, but the former images are also proven more beneficial for semantic nighttime segmentation in day-to-night adaptation. Code and data will be made publicly available.

Related papers

• Real-time 3D-aware Portrait Video Relighting [89.41078798641732]
  We present the first real-time 3D-aware method for relighting in-the-wild videos of talking faces based on Neural Radiance Fields (NeRF) We infer an albedo tri-plane, as well as a shading tri-plane based on a desired lighting condition for each video frame with fast dual-encoders. Our method runs at 32.98 fps on consumer-level hardware and achieves state-of-the-art results in terms of reconstruction quality, lighting error, lighting instability, temporal consistency and inference speed.
  arXiv  Detail & Related papers  (2024-10-24T01:34:11Z)
• Exploring Reliable Matching with Phase Enhancement for Night-time Semantic Segmentation [58.180226179087086]
  We propose a novel end-to-end optimized approach, named NightFormer, tailored for night-time semantic segmentation. Specifically, we design a pixel-level texture enhancement module to acquire texture-aware features hierarchically with phase enhancement and amplified attention. Our proposed method performs favorably against state-of-the-art night-time semantic segmentation methods.
  arXiv  Detail & Related papers  (2024-08-25T13:59:31Z)
• Lite2Relight: 3D-aware Single Image Portrait Relighting [87.62069509622226]
  Lite2Relight is a novel technique that can predict 3D consistent head poses of portraits. By utilizing a pre-trained geometry-aware encoder and a feature alignment module, we map input images into a relightable 3D space. This includes producing 3D-consistent results of the full head, including hair, eyes, and expressions.
  arXiv  Detail & Related papers  (2024-07-15T07:16:11Z)
• A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint [19.723367790947684]
  We propose a semi-supervised model for real-world nighttime dehazing. First, the spatial attention and frequency spectrum filtering are implemented as a spatial-frequency domain information interaction module. Second, a pseudo-label-based retraining strategy and a local window-based brightness loss for semi-supervised training process is designed to suppress haze and glow.
  arXiv  Detail & Related papers  (2024-03-27T13:27:02Z)
• When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimation [47.617222712429026]
  We show how to use a combination of three techniques to allow the existing photometric losses to work for both day and nighttime images. First, we introduce a per-pixel neural intensity transformation to compensate for the light changes that occur between successive frames. Second, we predict a per-pixel residual flow map that we use to correct the reprojection correspondences induced by the estimated ego-motion and depth.
  arXiv  Detail & Related papers  (2022-06-28T09:29:55Z)
• Nighttime Dehazing with a Synthetic Benchmark [147.21955799938115]
  We propose a novel synthetic method called 3R to simulate nighttime hazy images from daytime clear images. We generate realistic nighttime hazy images by sampling real-world light colors from a prior empirical distribution. Experiment results demonstrate their superiority over state-of-the-art methods in terms of both image quality and runtime.
  arXiv  Detail & Related papers  (2020-08-10T02:16:46Z)
• Night-time Scene Parsing with a Large Real Dataset [67.11211537439152]
  We aim to address the night-time scene parsing (NTSP) problem, which has two main challenges. To tackle the scarcity of night-time data, we collect a novel labeled dataset, named it NightCity, of 4,297 real night-time images. We also propose an exposure-aware framework to address the NTSP problem through augmenting the segmentation process with explicitly learned exposure features.
  arXiv  Detail & Related papers  (2020-03-15T18:11:34Z)

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.