Encoding normal vectors using optimized spherical coordinates

Smith, Jeanon N.; Petrova, Guergana; Schaefer, Scott

doi:10.1016/j.cag.2012.03.017

Cited by 8 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we propose a compression scheme for the compression of triplets of single-precision numbers which occur frequently in computational physics. This method extends the work of Smith et al [20] to 3D vectors of arbitrary length. These techniques allow for unstructured data access and in-line compression and decompression, with approximate symmetry in compression/decompression times.…”

Section: Introductionmentioning

confidence: 52%

“…It has also been shown by Meyer et al [19] that 51-bits is sufficient to losslessly represent unit vectors formed of three 32bit floating-point numbers. Some more recent work presented by Smith et al [20] looked to apply lossy compression to this case with increased efficiency. The approach taken quantised 3D unit vectors by initially transforming them into spherical coordinates, and subsequently discretising the two angles in bins.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inline Vector Compression for Computational Physics

Trojak¹,

Witherden²

2020

Preprint

View full text Add to dashboard Cite

A novel inline data compression method is presented for single-precision vectors in three dimensions. The primary application of the method is for accelerating computational physics calculations where the throughput is bound by memory bandwidth. The scheme employs spherical polar coordinates, angle quantisation, and a bespoke floating-point representation of the magnitude to achieve a fixed compression ratio of 1.5. The anisotropy of this method is considered, along with companding and fractional splitting techniques to improve the efficiency of the representation. We evaluate the scheme numerically within the context of high-order computational fluid-dynamics. For both the isentropic convecting vortex and the Taylor-Green vortex test cases the results are found to be comparable to those without compression. Performance is evaluated for a vector addition kernel on an NVIDIA Titan V GPU; it is demonstrated that a speedup of 1.5 can be achieved.

show abstract

Section: Introductionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Inline Vector Compression for Computational Physics

Trojak¹,

Witherden²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…For run-time access during rendering, we adopt a simple table-based quantization technique where a preprocessed lookup table, indexed with a 16-bit unsigned integer, provides up to 65,536 (=2 16 ) discrete unit normal samples. To fill the table, we use the normal encoding/decoding method of Smith et al [24], which exhibits the lowest mean and maximum angular errors among a variety of 16-bit unit normal representation techniques [25]. Once a lookup table is prepared, the original 12-byte unit normal (n x , n y , n z ) of a vertex is transformed into a two-byte index whose corresponding direction is closest to that of the original vector.…”

Section: Quantization Of Vertex Attributesmentioning

confidence: 99%

Efficient Ray Tracing of Large 3D Scenes for Mobile Distributed Computing Environments

Seo

Park

Ihm

2022

Sensors

View full text Add to dashboard Cite

Cluster computing has attracted much attention as an effective way of solving large-scale problems. However, only a few attempts have been made to explore mobile computing clusters that can be easily built using commodity smartphones and tablets. To investigate the possibility of mobile cluster-based rendering of large datasets, we developed a mobile GPU ray tracer that renders nontrivial 3D scenes with many millions of triangles at an interactive frame rate on a small-scale mobile cluster. To cope with the limited processing power and memory space, we first present an effective 3D scene representation scheme suitable for mobile GPU rendering. Then, to avoid performance impairment caused by the high latency and low bandwidth of mobile networks, we propose using a static load balancing strategy, which we found to be more appropriate for the vulnerable mobile clustering environment than a dynamic strategy. Our mobile distributed rendering system achieved a few frames per second when ray tracing 1024 × 1024 images, using only 16 low-end smartphones, for large 3D scenes, some with more than 10 million triangles. Through a conceptual demonstration, we also show that the presented rendering scheme can be effectively explored for augmenting real scene images, captured or perceived by augmented and mixed reality devices, with high quality ray-traced images.

show abstract

“…A deep neural network, also based on Octree data, was then introduced in [12]. Notably, a fast compression algorithm was developed in [20] considering spherical voxels, while the Moving Picture Expert Group (MPEG) has released specifications for the videobased (V-PCC) and the geometry-based (G-PCC) point cloud compression standards [21].…”

Section: B 3d Data Compressionmentioning

confidence: 99%

“…HIGH (1 mm 2 ) Table I: List of Octree compression profiles according to the PCL [23]. one point (1).Then, all non-zero bytes are saved in breadthfirst order [20]. PCL offers 12 different resolution profiles (corresponding to 12 different levels of compression), which can be grouped into 3 categories, i.e., HIGH, MEDIUM, and LOW, as reported in Table I.…”

Section: B 3d Data Compressionmentioning

confidence: 99%

Point Cloud Compression for Efficient Data Broadcasting: A Performance Comparison

Nardo¹,

Peressoni²,

Testolina³

et al. 2022

Preprint

View full text Add to dashboard Cite

The worldwide commercialization of fifth generation (5G) wireless networks and the exciting possibilities offered by connected and autonomous vehicles (CAVs) are pushing toward the deployment of heterogeneous sensors for tracking dynamic objects in the automotive environment. Among them, Light Detection and Ranging (LiDAR) sensors are witnessing a surge in popularity as their application to vehicular networks seem particularly promising. LiDARs can indeed produce a threedimensional (3D) mapping of the surrounding environment, which can be used for object detection, recognition, and topography. These data are encoded as a point cloud which, when transmitted, may pose significant challenges to the communication systems as it can easily congest the wireless channel. Along these lines, this paper investigates how to compress point clouds in a fast and efficient way. Both 2D-and a 3D-oriented approaches are considered, and the performance of the corresponding techniques is analyzed in terms of (de)compression time, efficiency, and quality of the decompressed frame compared to the original. We demonstrate that, thanks to the matrix form in which LiDAR frames are saved, compression methods that are typically applied for 2D images give equivalent results, if not better, than those specifically designed for 3D point clouds.

show abstract

Encoding normal vectors using optimized spherical coordinates

Cited by 8 publications

References 12 publications

Inline Vector Compression for Computational Physics

Inline Vector Compression for Computational Physics

Efficient Ray Tracing of Large 3D Scenes for Mobile Distributed Computing Environments

Point Cloud Compression for Efficient Data Broadcasting: A Performance Comparison

Contact Info

Product

Resources

About