The automatic sensing and analysis of 3-D surface points from visual scenes

technical reportDescribed are the design and implementation of a new range-measuring sensing device and an associated software algorithm for constructing surface descriptions of arbitrary three-dimensional objects from single or multiple views. The sensing device, which measures surface points from objects in its environment, is a computer-controlled, random-access, triangulating rangefinder with a mirror-deflected laser beam and revolving disc detectors. The algorithm developed processes these surface points and generates, in a deterministic fashion, complete surface descriptions of all encountered objects. In its processing, the algorithm also detects parts of objects for which there is insufficient data, and can supply the sensing device with the control parameters needed to successfully measure the uncharted regions. The resulting object descriptions are suitable for use in a number of areas, such as computer graphics, where the process of constructing object definitions has heretofore been very tedious. Together with the sensing device, this approach to object description can be utilized in a variety of scene analysis and pattern recognition applications which involve interaction with "real world", three-dimensional objects

Distributing a visible surface algorithm over multiple processors

Described is a procedure for executing a visible surface algorithm in a new multi-microprocessor system which utilizes distributed image and depth ("Z") buffers. It is shown that despite image distribution over a large number of processing and memory units, object coherence can still be maintained and used to reduce the number of calculations needed to generate a continuous-tone visible surface image

Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling

We present a method that enables synthesizing novel views and novel poses of arbitrary human performers from sparse multi-view images. A key ingredient of our method is a hybrid appearance blending module that combines the advantages of the implicit body NeRF representation and image-based rendering. Existing generalizable human NeRF methods that are conditioned on the body model have shown robustness against the geometric variation of arbitrary human performers. Yet they often exhibit blurry results when generalized onto unseen identities. Meanwhile, image-based rendering shows high-quality results when sufficient observations are available, whereas it suffers artifacts in sparse-view settings. We propose Neural Image-based Avatars (NIA) that exploits the best of those two methods: to maintain robustness under new articulations and self-occlusions while directly leveraging the available (sparse) source view colors to preserve appearance details of new subject identities. Our hybrid design outperforms recent methods on both in-domain identity generalization as well as challenging cross-dataset generalization settings. Also, in terms of the pose generalization, our method outperforms even the per-subject optimized animatable NeRF methods. The video results are available at https://youngjoongunc.github.io/ni

The Delta Tree: An Object-Centered Approach to Image-Based Rendering

This paper introduces the delta tree, a data structure that represents an object using a set of reference images. It also describes an algorithm for generating arbitrary re-projections of an object by traversing its delta tree. Delta trees are an efficient representation in terms of both storage and rendering performance. Each node of a delta tree stores an image taken from a point on a sampling sphere that encloses the object. Each image is compressed by discarding pixels that can be reconstructed by warping its ancestor's images to the node's viewpoint. The partial image stored at each node is divided into blocks and represented in the frequency domain. The rendering process generates an image at an arbitrary viewpoint by traversing the delta tree from a root node to one or more of its leaves. A subdivision algorithm selects only the required blocks from the nodes along the path. For each block, only the frequency components necessary to reconstruct the final image at an appropriate sampling density are used. This frequency selection mechanism handles both antialiasing and level-of-detail within a single framework. A complex scene is initially rendered by compositing images generated by traversing the delta trees of its components. Once the reference views of a scene are rendered once in this manner, the entire scene can be reprojected to an arbitrary viewpoint by traversing its own delta tree. Our approach is limited to generating views of an object from outside the object's convex hull. In practice we work around this problem by subdividing objects to render views from within the convex hull

Combining Head-Mounted and Projector-Based Displays for Surgical Training

We introduce and present preliminary results for a hybrid display system combining head-mounted and projector-based displays. Our work is motivated by a surgical training application, where it is necessary to simultaneously provide both a high-fidelity view of a central close-up task (the surgery) and visual awareness of objects and events in the surrounding environment. In particular, for trauma surgeons it would be valuable to learn to work in an environment that is realistically filled with both necessary and distracting objects and events

Focus 3D: Compressive Accommodation Display

We present a glasses-free 3D display design with the potential to provide viewers with nearly correct accommodative depth cues, as well as motion parallax and binocular cues. Building on multilayer attenuator and directional backlight architectures, the proposed design achieves the high angular resolution needed for accommodation by placing spatial light modulators about a large lens: one conjugate to the viewer's eye, and one or more near the plane of the lens. Nonnegative tensor factorization is used to compress a high angular resolution light field into a set of masks that can be displayed on a pair of commodity LCD panels. By constraining the tensor factorization to preserve only those light rays seen by the viewer, we effectively steer narrow high-resolution viewing cones into the user's eyes, allowing binocular disparity, motion parallax, and the potential for nearly correct accommodation over a wide field of view. We verify the design experimentally by focusing a camera at different depths about a prototype display, establish formal upper bounds on the design's accommodation range and diffraction-limited performance, and discuss practical limitations that must be overcome to allow the device to be used with human observers

Bringing Telepresence to Every Desk

In this paper, we work to bring telepresence to every desktop. Unlike commercial systems, personal 3D video conferencing systems must render high-quality videos while remaining financially and computationally viable for the average consumer. To this end, we introduce a capturing and rendering system that only requires 4 consumer-grade RGBD cameras and synthesizes high-quality free-viewpoint videos of users as well as their environments. Experimental results show that our system renders high-quality free-viewpoint videos without using object templates or heavy pre-processing. While not real-time, our system is fast and does not require per-video optimizations. Moreover, our system is robust to complex hand gestures and clothing, and it can generalize to new users. This work provides a strong basis for further optimization, and it will help bring telepresence to every desk in the near future. The code and dataset will be made available on our website https://mcmvmc.github.io/PersonalTelepresence/