Xiaoshi Zhou

Anti-aliasing is a crucial research topic in computer graphics, which can significantly enhance the rendering quality of neural radiance fields (NeRF). Recent studies have introduced effective anti-aliasing NeRF methods, utilizing cone-casting to replace ray-casting and modeling the 3D observation area of pixels as circular cones. The cone-casting strategy has successfully reduced blurring and aliasing in novel view rendering. However, we have observed that the light cones are not standard circular cones because the camera projection model distorts them into elliptic cones of diverse sizes and shapes. This finding motivates us to model pixel light cones as anisotropic elliptic cones and propose an elliptic cone-casting-based anti-aliasing NeRF method called “ECC-NeRF". Specifically, we first derive the elliptic cone models for common pinhole, fisheye, and panoramic cameras based on their camera projection models. Then, we integrate the proposed elliptic cone-casting into two representative cone-casting-based anti-aliasing NeRF methods: Mip-NeRF and Zip-NeRF. Our experimental evaluations on multiple datasets demonstrate that our method can achieve more accurate multi-scale anisotropic representation and better novel view rendering quality with negligible additional computation cost.

In this paper, we present an end-to-end holographic video conferencing system that enables real-time high-quality free-viewpoint rendering of participants in different spatial regions, placing them in a unified virtual space for a more immersive display. Our system offers a cost-effective, complete holographic conferencing process, including multiview 3D data capture, RGB-D stream compression and transmission, high-quality rendering, and immersive display. It employs a sparse set of commodity RGB-D cameras that capture 3D geometric and textural information. We then remotely transmit color and depth maps via standard video encoding and transmission protocols. We propose a GPU-parallelized rendering pipeline based on an image-based virtual view synthesis algorithm to achieve real-time and high-quality scene rendering. This algorithm uses an on-the-fly Truncated Signed Distance Function (TSDF) approach, which marches along virtual rays within a computed precise search interval to determine surface intersections. We then design a multiweight projective texture mapping method to fuse color information from multiple views. Furthermore, we introduce a method that uses a depth confidence map to weight the rendering results from different views, which mitigates the impact of sensor noise and inaccurate measurements on the rendering results. Finally, our system places conference participants from different spaces into a virtual conference environment with a global coordinate system through coordinate transformation, which simulates a real conference scene in physical space, providing an immersive remote conferencing experience. Experimental evaluations confirm our system’s real-time, low-latency, high-quality, and immersive capabilities.

Achieving precise synchronization is critical for multi-camera systems in various applications. Traditional methods rely on hardware-triggered synchronization, necessitating significant manual effort to connect and adjust synchronization cables, especially with multiple cameras involved. This not only increases labor costs but also restricts scene layout and incurs high setup expenses. To address these challenges, we propose a novel subframe synchronization technique for multi-camera systems that operates without the need for additional hardware triggers. Our approach leverages a time-calibrated video featuring specific markers and a uniformly moving ball to accurately extract the temporal relationship between local and global time systems across cameras. This allows for the calculation of new timestamps and precise frame-level alignment. By employing interpolation algorithms, we further refine synchronization to the subframe level. Experimental results validate the robustness and high temporal precision of our method, demonstrating its adaptability and potential for use in demanding multi-camera setups.

Free-viewpoint videos offer audiences a more immersive and liberated way to watch sports. The rendering of sports scenes encompasses two essential elements: dynamic targets and static scenes. While much current research focuses on achieving high-quality rendering of human bodies, rendering large-scale sports scenes presents various challenges. Sports arenas are characterized by large spatial extents, restricted camera placement, uncontrollable lighting, weak textures, and repetitive patterns, all of which pose significant obstacles to achieving high-quality scene rendering. In this work, we propose a neural radiance field rendering method based on scene-prior geometric constraints. We introduce prior 3D geometric dimensions and 2D semantic masks, to derive high-precision ground plane depth maps from camera imaging parameters. This is a geometry-based method that does not rely on visual features, and thus it is unaffected by insufficient textures, repetition, and reflections. Subsequently, we apply ground depth maps as geometric consistency constraints to optimize the neural rendering network, thereby reducing the impact of color inconsistencies across viewpoints. The visual sensor network we build can synchronously capture static fields and dynamic targets in sports scenes. Based on the visual sensor network, we collected multiviewpoint datasets of large-scale sports scenes at Invengo and Xidian Gymnasium for performance evaluation. Experimental results demonstrate that our method can generate high-precision and cross-viewpoint scale-consistent depth constraints, and helps reduce holes and artifacts in synthesized views. Our method outperforms the state-of-the-art for novel view rendering for challenging large-scale sports scenes.