Presentation
EBREnv: SVBRDF Estimation in Uncontrolled Environment Lighting via Exemplar-Based Representation
DescriptionRecovering spatial-varying bi-directional reflectance distribution function (SVBRDF) from as few as possible captured images has been a challenging task in computer graphics.
Benefiting from the co-located flashlight-camera capture strategy and data-driven priors, SVBRDF can be estimated from few input images.
However, this capture strategy usually requires a controllable darkroom environment, ensuring the flashlight is a single light source. It is often impractical during on-site capture in real-world scenarios.
To support SVBRDF estimation in an uncontrolled environment, the key challenge lies in the high-precise estimation of unknown environment lighting and its effective utilization on SVBRDF recovery.
To address this issue, we proposed a novel exemplar-based environment lighting representation, which is easier to use for neural networks.
These exemplars are a set of rendered images of selected materials under the environment lighting.
By embedding the rendering process, our approach transforms environment lighting represented in the spherical domain into the sample-surface domain, thereby achieving the domain alignment with input images.
This significantly reduces the network's learning burden, resulting in a more precise environment lighting estimation.
Furthermore, after lighting prediction, we also present a dominant lighting extraction algorithm and an adaptive exemplar selection algorithm to enhance the guidance of environment lighting in SVBRDF estimation.
Finally, considering the distant contribution of environment lighting and point lighting to SVBRDF recovery, we proposed a well-designed cascaded network.
Quantitative assessments and qualitative analysis have demonstrated that our method achieves superior SVBRDF estimations compared to previous approaches.
The source code will be released.
Benefiting from the co-located flashlight-camera capture strategy and data-driven priors, SVBRDF can be estimated from few input images.
However, this capture strategy usually requires a controllable darkroom environment, ensuring the flashlight is a single light source. It is often impractical during on-site capture in real-world scenarios.
To support SVBRDF estimation in an uncontrolled environment, the key challenge lies in the high-precise estimation of unknown environment lighting and its effective utilization on SVBRDF recovery.
To address this issue, we proposed a novel exemplar-based environment lighting representation, which is easier to use for neural networks.
These exemplars are a set of rendered images of selected materials under the environment lighting.
By embedding the rendering process, our approach transforms environment lighting represented in the spherical domain into the sample-surface domain, thereby achieving the domain alignment with input images.
This significantly reduces the network's learning burden, resulting in a more precise environment lighting estimation.
Furthermore, after lighting prediction, we also present a dominant lighting extraction algorithm and an adaptive exemplar selection algorithm to enhance the guidance of environment lighting in SVBRDF estimation.
Finally, considering the distant contribution of environment lighting and point lighting to SVBRDF recovery, we proposed a well-designed cascaded network.
Quantitative assessments and qualitative analysis have demonstrated that our method achieves superior SVBRDF estimations compared to previous approaches.
The source code will be released.
Event Type
Technical Papers
TimeThursday, 18 December 20259:10am - 9:21am HKT
LocationMeeting Room S426+S427, Level 4