Adaptive depth bias for shadow maps

Dou, Hang; Yan, Ying; Kerzner, Ethan; Dai, Zeng; Wyman, Chris

doi:10.1145/2556700.2556706

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…The usage of shadow maps presents some limitations: it is not possible to have partial light occlusion from translucid objects, shadows are projected for a finite area, and the number of lights casting shadows must be limited, since computational cost and memory increase exponentially with the number of lights in the scene. Numeric precision limitation may also cause a shimmering effect commonly known as “shadow acne”, that was studied and addressed in [ 37 ].…”

Section: Proposed Solutionmentioning

confidence: 99%

Environment-Aware Rendering and Interaction in Web-Based Augmented Reality

et al. 2023

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This work presents a novel framework for web-based environment-aware rendering and interaction in augmented reality based on WebXR and three.js. It aims at accelerating the development of device-agnostic Augmented Reality (AR) applications. The solution allows for a realistic rendering of 3D elements, handles geometry occlusion, casts shadows of virtual objects onto real surfaces, and provides physics interaction with real-world objects. Unlike most existing state-of-the-art systems that are built to run on a specific hardware configuration, the proposed solution targets the web environment and is designed to work on a vast range of devices and configurations. Our solution can use monocular camera setups with depth data estimated by deep neural networks or, when available, use higher-quality depth sensors (e.g., LIDAR, structured light) that provide a more accurate perception of the environment. To ensure consistency in the rendering of the virtual scene a physically based rendering pipeline is used, in which physically correct attributes are associated with each 3D object, which, combined with lighting information captured by the device, enables the rendering of AR content matching the environment illumination. All these concepts are integrated and optimized into a pipeline capable of providing a fluid user experience even on middle-range devices. The solution is distributed as an open-source library that can be integrated into existing and new web-based AR projects. The proposed framework was evaluated and compared in terms of performance and visual features with two state-of-the-art alternatives.

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Section: Proposed Solutionmentioning

confidence: 99%

Environment-Aware Rendering and Interaction in Web-Based Augmented Reality

et al. 2023

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“…Such bumps, when used to compute attached shadows will lead to several false positives: normals computed from bumpy depth will make surfaces sporadically face away from the light, i. e., darker. When used during the computation of detached shadows, they cast small shadows known as “shadow acne” in the shadow mapping literature [DYK∗14], this phenomenon is illustrated in the first column of Fig. 4.…”

Section: Image Relighting Using Approximate Depth Mapsmentioning

confidence: 99%

OutCast: Outdoor Single‐image Relighting with Cast Shadows

Griffiths

Ritschel

Philip

2022

Computer Graphics Forum

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We propose a relighting method for outdoor images. Our method mainly focuses on predicting cast shadows in arbitrary novel lighting directions from a single image while also accounting for shading and global effects such the sun light color and clouds. Previous solutions for this problem rely on reconstructing occluder geometry, e. g., using multi‐view stereo, which requires many images of the scene. Instead, in this work we make use of a noisy off‐the‐shelf single‐image depth map estimation as a source of geometry. Whilst this can be a good guide for some lighting effects, the resulting depth map quality is insufficient for directly ray‐tracing the shadows. Addressing this, we propose a learned image space ray‐marching layer that converts the approximate depth map into a deep 3D representation that is fused into occlusion queries using a learned traversal. Our proposed method achieves, for the first time, state‐of‐the‐art relighting results, with only a single image as input. For supplementary material visit our project page at: http://dgriffiths.uk/outcast.

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“…Shadow mapping [Williams 1978] and its variants are efficient shadowing methods for point and directional lights in dynamic scenes. Shadow map resolution and projection leads to shadow aliasing artifacts, with solutions (e.g., depth biasing [Dou et al 2014;King 2004]) leading to secondary issues and trade-offs. Modern shadow mapping relies on delicately engineered systems that combine many cascaded maps [Engel 2006;Zhang et al 2006].…”

Section: Related Workmentioning

confidence: 99%

Neural Shadow Mapping

Datta

Nowrouzezahrai

Schied³

et al. 2022

Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings

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Figure 1: Our hard and soft shadowing method approaches the quality of offline ray tracing whilst striking a favorable position on the performance-accuracy spectrum. On the high-performance end, we produce higher quality results than 𝑛 × 𝑛 Moment Shadow Maps (MSM-𝑛). We require only vanilla shadow mapping inputs to generate visual (and temporal) results that approach ray-traced reference, surpassing more costly denoised interactive ray-traced methods.

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Adaptive depth bias for shadow maps

Cited by 9 publications

References 17 publications

Environment-Aware Rendering and Interaction in Web-Based Augmented Reality

Environment-Aware Rendering and Interaction in Web-Based Augmented Reality

OutCast: Outdoor Single‐image Relighting with Cast Shadows

Neural Shadow Mapping

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