Real-time Monte Carlo (MC) ray tracing with low sampling rates demands a denoising algorithm that adeptly balances the trade-off between quality and efficiency. Previous works have paid much attention on designing delicate denoising architecture while ignoring model compression. In this work, we present a render-aware knowledge distillation (RAKD) framework, specifically designed for Monte Carlo denoising. We meticulously delineate the Knowledge Distillation (KD) process within RAKD, emphasizing three pivotal techniques: the strategic incorporation of an auxiliary unlabeled dataset, the integration of adversarial learning through generative adversarial network (GAN), and the application of parameter transfer for robust model initialization. These approaches are harmoniously combined to distill knowledge effectively, enabling our student model to adeptly strike a balance between preserving high-frequency details and reducing low-frequency noise. Finally, our results demonstrate that RAKD achieves state-of-the-art quality while upholding real-time performance, successfully tackling the computational constraints faced by resource-limited devices.

Systematically simulating specular light transport requires an exhaustive search for triangle tuples containing admissible paths. Given the extreme inefficiency of enumerating all combinations, we significantly reduce the search domain by stochastically sampling such tuples. The challenge is to design proper sampling probabilities that keep the noise level controllable. Our key insight is that by bounding the irradiance contributed by each triangle tuple at a given position, we can sample a subset of triangle tuples with potentially high contributions. Although low-contribution tuples are assigned a negligible probability, the overall variance remains low. Therefore, we derive position and irradiance bounds for caustics casted by each triangle tuple, introducing a bounding property of rational functions on a Bernstein basis. When formulating position and irradiance expressions into rational functions, we handle non-rational parts through remainder variables to maintain bounding validity. Finally, we carefully design the sampling probabilities by optimizing the upper bound of the variance, expressed only using the position and irradiance bounds. The bound-driven sampling of triangle tuples is intrinsically unbiased even without defensive sampling. It can be combined with various unbiased and biased root-finding techniques within a local triangle domain. Extensive evaluations show that our method enables the fast and reliable rendering of complex caustics effects. Yet, our method is efficient for no more than two specular vertices, where complexity grows sublinearly to the number of triangles and linearly to that of emitters, and does not consider the Fresnel and visibility terms. We also rely on parameters to control subdivisions.

Material appearance is a key component of photorealism, with a pronounced impact on human perception. Although there are many prior works targeting at measuring opaque materials using light-weight setups (e.g., consumer-level cameras), little attention is paid on acquiring the optical properties of translucent materials which are also quite common in nature. In this paper, we present a practical method for acquiring scattering properties of translucent materials, based solely on ordinary images captured with unknown lighting and camera parameters. The key to our method is an inter-pixel translucency prior which states that image pixels of a given homogeneous translucent material typically form curves (dubbed translucent curves) in the RGB space, of which the shapes are determined by the parameters of the material. We leverage this prior in a specially-designed convolutional neural network comprising multiple encoders, a translucency-aware feature fusion module and a cascaded decoder. We demonstrate, through both visual comparisons and quantitative evaluations, that high accuracy can be achieved on a wide range of real-world translucent materials.

Inserting 3D virtual objects into real-world images has many applications in photo editing and augmented reality. One key issue to ensure the reality of the composite whole scene is to generate consistent shadows between virtual and real objects. However, it is challenging to synthesize visually realistic shadows for virtual and real objects without any explicit geometric information of the real scene or manual intervention, especially for the shadows on the virtual objects projected by real objects. In view of this challenge, we present, to our knowledge, the first end-to-end solution to fully automatically project real shadows onto virtual objects for outdoor scenes. In our method, we introduce the Shifted Shadow Map, a new shadow representation that encodes the binary mask of shifted real shadows after inserting virtual objects in an image. Based on the shifted shadow map, we propose a CNN-based shadow generation model named ShadowMover which first predicts the shifted shadow map for an input image and then automatically generates plausible shadows on any inserted virtual object. A large-scale dataset is constructed to train the model. Our ShadowMover is robust to various scene configurations without relying on any geometric information of the real scene and is free of manual intervention. Extensive experiments validate the effectiveness of our method.