We present a real-time bidirectional communication system that lets two people, separated by distance, experience a face-to-face conversation as if they were copresent. It is the first telepresence system that is demonstrably better than 2D videoconferencing, as measured using participant ratings (e.g., presence, attentiveness, reaction-gauging, engagement), meeting recall, and observed nonverbal behaviors (e.g., head nods, eyebrow movements). This milestone is reached by maximizing audiovisual fidelity and the sense of copresence in all design elements, including physical layout, lighting, face tracking, multi-view capture, microphone array, multi-stream compression, loudspeaker output, and lenticular display. Our system achieves key 3D audiovisual cues (stereopsis, motion parallax, and spatialized audio) and enables the full range of communication cues (eye contact, hand gestures, and body language), yet does not require special glasses or body-worn microphones/headphones. The system consists of a head-tracked autostereoscopic display, high-resolution 3D capture and rendering subsystems, and network transmission using compressed color and depth video streams. Other contributions include a novel image-based geometry fusion algorithm, free-space dereverberation, and talker localization.

We propose dual-domain filtering, an image processing paradigm that couples spatial domain with frequency domain filtering. Our dual-domain defined filter removes artifacts like residual noise of other image denoising methods and compression artifacts. Moreover, iterating the filter achieves state-of-the-art image denoising results, but with a much simpler algorithm than competing approaches. The simplicity and versatility of the dual-domain filter makes it an attractive tool for image processing.

Image denoising continues to be an active research topic. Although state-of-the-art denoising methods are numerically impressive and approch theoretical limits, they suffer from visible artifacts.While they produce acceptable results for natural images, human eyes are less forgiving when viewing synthetic images. At the same time, current methods are becoming more complex, making analysis, and implementation difficult. We propose image denoising as a simple physical process, which progressively reduces noise by deterministic annealing. The results of our implementation are numerically and visually excellent. We further demonstrate that our method is particularly suited for synthetic images. Finally, we offer a new perspective on image denoising using robust estimators.

Image denoising methods have been implemented in both spatial and transform domains. Each domain has its advantages and shortcomings, which can be complemented by each other. State-of-the-art methods like block-matching 3D filtering (BM3D) therefore combine both domains. However, implementation of such methods is not trivial. We offer a hybrid method that is surprisingly easy to implement and yet rivals BM3D in quality.

We propose a novel approach for image space adaptive sampling and filtering in Monte Carlo rendering. We use an iterative scheme composed of three steps. First, we adaptively distribute samples in the image plane. Second, we denoise the image using a non-linear filter. Third, we estimate the residual per-pixel error of the filtered rendering, and the error estimate guides the sample distribution in the next iteration. The effectiveness of our approach hinges on the use of a state of the art image denoising technique, which we extend to an adaptive rendering framework. A key idea is to split the Monte Carlo samples into two buffers. This improves denoising performance and facilitates variance and error estimation. Our method relies only on the Monte Carlo samples, allowing us to handle arbitrary light transport and lens effects. In addition, it is robust to high noise levels and complex image content. We compare our approach to a state of the art adaptive rendering technique based on adaptive bandwidth selection and demonstrate substantial improvements in terms of both numerical error and visual quality. Our framework is easy to implement on top of standard Monte Carlo renderers and it incurs little computational overhead.

In this article we present a novel formulation of progressive photon mapping. Similar to the original progressive photon mapping algorithm, our approach is capable of computing global illumination solutions without bias in the limit, and it uses only a constant amount of memory. It produces high-quality results in situations that are difficult for most other algorithms, such as scenes with realistic light fixtures where the light sources are completely enclosed by refractive material. Our new formulation is based on a probabilistic derivation. The key property of our approach is that it does not require the maintenance of local photon statistics. In addition, our derivation allows for arbitrary kernels in the radiance estimate and includes stochastic ray tracing algorithms. Finally, our approach is readily applicable to volumetric photon mapping. We compare our algorithm to previous progressive photon mapping approaches and show that we achieve the same convergence to unbiased results, even without local photon statistics.

We introduce a novel approach for image space adaptive sampling and reconstruction in Monte Carlo rendering. We greedily minimize relative mean squared error (MSE) by iterating over two steps. First, given a current sample distribution, we optimize over a discrete set of filters at each pixel and select the filter that minimizes the pixel error. Next, given the current filter selection, we distribute additional samples to further reduce MSE. The success of our approach hinges on a robust technique to select suitable per pixel filters. We develop a novel filter selection procedure that robustly solves this problem even with noisy input data. We evaluate our approach using effects such as motion blur, depth of field, interreflections, etc. We provide a comparison to a state-of-the-art algorithm based on wavelet shrinkage and show that we achieve significant improvements in numerical error and visual image quality. Our approach is simple to implement, requires a single user parameter, and is compatible with standard Monte Carlo rendering.

With the increase in popularity of stereoscopic 3D imagery for film, TV, and interactive entertainment, an urgent need for editing tools to support stereo content creation has become apparent. In this paper we present an end-to-end system for object copy & paste in a stereoscopic setting to address this need. There is no straightforward extension of 2D copy & paste to support the addition of the third dimension as we show in this paper. For stereoscopic copy & paste we need to handle depth, and our core objective is to obtain a convincing 3D viewing experience. As one of the main contributions of our system, we introduce a stereo billboard method for stereoscopic rendering of the copied selection. Our approach preserves the stereo volume and is robust to the inevitable inaccuracies of the depth maps computed from a stereo pair of images. Our system also includes an interactive stereoscopic segmentation tool to achieve high quality object selection. Hence, we focus on intuitive and minimal user interaction, and our editing operations perform within interactive rates to provide immediate feedback.