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Figure 1: Visualization of sampling strategies (white pixels show a subset of the actual samples, missed geometry is marked red). Left: An urban input scene and a view cell (in yellow) for visibility sampling. Middle: Previous visibility sampling algorithms repeatedly sample the same triangles in the foreground while missing many smaller triangles and distant geometry. Right: Our solution is guided by scene visibility and therefore quickly finds most visible triangles while requiring drastically fewer samples than previous methods. This paper addresses the problem of computing the triangles visible from a region in space. The proposed aggressive visibility solution is based on stochastic ray shooting and can take any triangular model as input. We do not rely on connectivity information, volumetric occluders, or the availability of large occluders, and can therefore process any given input scene. The proposed algorithm is practically memoryless, thereby alleviating the large memory consumption problems prevalent in several previous algorithms. The strategy of our algorithm is to use ray mutations in ray space to cast rays that are likely to sample new triangles. Our algorithm improves the sampling efficiency of previous work by over two orders of magnitude.

Interactive visualization of massive models still remains a challenging problem. This is mainly due to a combination of ever increasing model complexity with the current hardware design trend that leads to a widening gap between slow data access speed and fast data processing speed. We argue that developing efficient data access and data management techniques is key in solving the problem of interactive visualization of massive models. Particularly, we discuss visibility culling, simplification, cache-coherent layouts, and data compression techniques as efficient data management techniques that enable interactive visualization of massive models.

Figure 1: A view of a large urban model consisting of 26.8M triangles. In the left image, the parts visible from a region located at the junction of two streets (in green) are colored. In the right image, only the buildings with some visible parts are displayed. From-region visibility culling is considered harder than from-point visibility culling, since it is inherently four-dimensional. We present a conservative occlusion culling method based on factorizing the 4D visibility problem into horizontal and vertical components. The visibility of the two components is solved asymmetrically: the horizontal component is based on a parameterization of the ray space, and the visibility of the vertical component is solved by incrementally merging umbrae. The technique is designed so that the horizontal and vertical operations can be efficiently realized together by modern graphics hardware. Similar to image-based from-point methods, we use an occlusion map to encode visibility; however, the image-space occlusion map is in the ray space rather than in the primal space. Our results show that the culling time and the size of the computed potentially visible set depend on the size of the viewcell. For moderate viewcells, conservative occlusion culling of large urban scenes takes less than a second, and the size of the potentially visible set is only about two times larger than the size of the exact visible set.

Visibility computation is crucial for computer graphics from its very beginning. The first visibility algorithms in computer graphics aimed to determine visible surfaces in a synthesized image of a 3D scene. Nowadays there are many different visibility algorithms for various visibility problems. We propose a new taxonomy of visibility problems that is based on a classification according to the problem domain. We provide a broad overview of visibility problems and algorithms in computer graphics grouped by the proposed taxonomy. The paper surveys visible surface algorithms, visibility culling algorithms, visibility algorithms for shadow computation, global illumination, point-based and image-based rendering, and global visibility computations. Finally, we discuss common concepts of visibility algorithm design and several criteria for the classification of visibility algorithms.

Abstract — We present visibility computation and data organization algorithms that enable high-fidelity walkthroughs of large 3D geometric datasets. A novel feature of our walkthrough system is that it performs work proportional only to the required detail in visible geometry at the rendering time. To accomplish this, we use a pre-computation phase that efficiently generates per-cell vLOD: the geometry visible from a view-region at the right level of detail. We encode changes between neighboring cells ’ vLODs, which are not required to be memory resident. At the rendering time, we incrementally construct the vLOD for the current view-cell and render it. We have a small CPU and memory requirement for rendering and are able to display models with tens of million polygons at interactive frame rates with less than one pixel screen-space deviation and accurate visibility.

In this paper, we show how recent GPUs can be used to very efficiently and conveniently sample the visibility between two surfaces, given a set of occluding triangles. We use bitwise arithmetics to evaluate, encode, and combine the samples blocked by each triangle. In particular, the number of operations is almost independent of the number of samples. Our method requires no CPU/GPU transfers, is fully implemented as geometric, vertex and fragment shaders, and thus does not impose to modify the way the geometry is sent to the graphics card. We finally present applications to soft shadows, and visibility analysis for level design.

Figure 1: Results of visibility computations after 1 minute of sampling. Visibility errors are marked in red. Left: Traditional per-view cell sampling. Middle: Adaptive Global Visibility Sampling. Right: Adaptive Global Visibility Sampling with visibility filter. Observe the severe underestimation of visibility in the left image. The visibility computed by our method in the middle produces significantly less visible artifacts. To the right, our method with a visibility filter applied is practically artifact-free. Note that during this minute, the potentially visible sets for all 8,192 view cells in this example model have been generated. In this paper we propose a global visibility algorithm which computes from-region visibility for all view cells simultaneously in a progressive manner. We cast rays to sample visibility interactions and use the information carried by a ray for all view cells it intersects. The main contribution of the paper is a set of adaptive sampling strategies based on ray mutations that exploit the spatial coherence of visibility. Our method achieves more than an order of magnitude speedup compared to per-view cell sampling. This provides a practical solution to visibility preprocessing and also enables a new type of interactive visibility analysis application, where it is possible to quickly inspect and modify a coarse global visibility solution that is constantly refined.

We present a fast exact from-region visibility algorithm for 2.5D urban scenes. The algorithm uses a subdivision of line space for identifying visibility interactions in a 2D footprint of the scene. Visibility in the remaining vertical dimension is resolved by testing for the existence of lines stabbing sequences of virtual portals. Our results show that exact analytic from-region visibility in urban scenes can be computed at times comparable or even superior to recent conservative methods.

This paper presents a novel method for computing visibility in 2.5D environments based on a novel theoretical result: the visibility from a region can be conservatively estimated by computing the visibility from a point using appropriately "shrunk" occluders and occludees. We show how approximate, yet conservative, shrunk objects can be efficiently computed in an urban environment. The technique provides a tighter potentially visible set (PVS) compared to the original method in which only occluders are shrunk. Finally, theoretical implications of the shrinking theorem are discussed, opening new research directions.