Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D scene data. Such data, if completely and well annotated, can serve as useful ingredients for a wide spectrum of computer vision and graphics works such as data-driven modeling and scene understanding, object detection and recognition. However, annotating a vast amount of 3D scene data remains challenging due to the lack of an effective tool and/or the complexity of 3D scenes (e.g. clutter, varying illumination conditions). This paper aims to build a robust annotation tool that effectively and conveniently enables the segmentation and annotation of massive 3D data. Our tool works by coupling 2D and 3D information via an interactive framework, through which users can provide high-level semantic annotation for objects. We have experimented our tool and found that a typical indoor scene could be well segmented and annotated in less than 30 minutes by using the tool, as opposed to a few hours if done manually. Along with the tool, we created a dataset of over a hundred 3D scenes associated with complete annotations using our tool. Both the tool and dataset will be available at http://scenenn.net.

Modern artificial intelligence offers a novel and transformative approach to creating digital art across diverse styles and modalities like images, videos and 3D data, unleashing the power of creativity and revolutionizing the way that we perceive and interact with visual content. This paper reports on recent advances in stylized 3D asset creation and manipulation with the expressive power of neural networks. We establish a taxonomy for neural stylization, considering crucial design choices such as scene representation, guidance data, optimization strategies, and output styles. Building on such taxonomy, our survey first revisits the background of neural stylization on 2D images, and then presents in-depth discussions on recent neural stylization methods for 3D data, accompanied by a benchmark evaluating selected mesh and neural field stylization methods. Based on the insights gained from the survey, we highlight the practical significance, open challenges, future research, and potential impacts of neural stylization, which facilitates researchers and practitioners to navigate the rapidly evolving landscape of 3D content creation using modern artificial intelligence.

Image-space denoising techniques have been widely employed in Monte Carlo rendering, typically blending neighboring pixel estimates using a denoising kernel. It is widely recognized that a kernel should be adapted to characteristics of the input pixel estimates in order to ensure robustness to diverse image features and amount of noise. Denoising with such an input-dependent kernel, however, can introduce a bias that makes the denoised estimate even less accurate than the noisy input estimate. Consequently, it has been considered essential to balance the bias introduced by denoising and the reduction of noise. We propose a new framework to define an input-dependent kernel that departs from the existing approaches based on error estimation or supervised learning. Rather than seeking an optimal bias-noise balance as in those existing approaches, we propose to constrain the amount of bias introduced by denoising. Such a constraint is made possible by the concept of uncorrelated statistics, which has never been applied for denoising. By designing an input-dependent kernel with uncorrelated weights against the input pixel estimates, our denoising kernel can reduce data-dependent noise with a negligible amount of bias in most cases. We demonstrate the effectiveness of our method for various scenes.