NeRC: Rendering Planar Caustics by Learning Implicit Neural Representations.

Caustics are challenging light transport effects for photo-realistic rendering. Photon mapping techniques play a fundamental role in rendering caustics. However, photon mapping methods render single caustics under the stationary light source in a fixed scene view. They require significant storage and computing resources to produce high-quality results. In this paper, we propose efficiently rendering more diverse caustics of a scene with the camera and the light source moving. We present a novel learning-based volume rendering approach with implicit representations for our proposed task. Considering the variety of materials and textures of planar caustic receivers, we decompose the output appearance into two components: the diffuse and specular parts with a probabilistic module. Unlike NeRF, we construct weights for rendering each component from the implicit signed distance function (SDF). Moreover, we introduce the centering calibration and the sine activation function to improve the performance of the color prediction network. Extensive experiments on the synthetic and real-world datasets illustrate that our method achieves much better performance than baselines in the quantitative and qualitative comparison, for rendering caustics in novel views with the dynamic light source. Especially, our method outperforms the baseline on the temporal consistency across frames. Code will be available at https://github.com/JiaxiongQ/NeRC.

SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Features Recombining.

The channel redundancy of convolutional neural networks (CNNs) results in the large consumption of memories and computational resources. In this work, we design a novel Slim Convolution (SlimConv) module to boost the performance of CNNs by reducing channel redundancies. Our SlimConv consists of three main steps: Reconstruct, Transform, and Fuse. It aims to reorganize and fuse the learned features more efficiently, such that the method can compress the model effectively. Our SlimConv is a plug-and-play architectural unit that can be used to replace convolutional layers in CNNs directly. We validate the effectiveness of SlimConv by conducting comprehensive experiments on various leading benchmarks, such as ImageNet, MS COCO2014, Pascal VOC2012 segmentation, and Pascal VOC2007 detection datasets. The experiments show that SlimConv-equipped models can achieve better performances consistently, less consumption of memory and computation resources than non-equipped counterparts. For example, the ResNet-101 fitted with SlimConv achieves 77.84% top-1 classification accuracy with 4.87 GFLOPs and 27.96M parameters on ImageNet, which shows almost 0.5% better performance with about 3 GFLOPs and 38% parameters reduced.