The Experts below are selected from a list of 124743 Experts worldwide ranked by ideXlab platform
Pat Hanrahan - One of the best experts on this subject based on the ideXlab platform.
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all frequency shadows using non linear wavelet lighting approximation
International Conference on Computer Graphics and Interactive Techniques, 2003Co-Authors: Ravi Ramamoorthi, Pat HanrahanAbstract:We present a method, based on pre-computed light transport, for real-time rendering of objects under all-frequency, time-varying illumination represented as a high-resolution Environment Map. Current techniques are limited to small area lights, with sharp shadows, or large low-frequency lights, with very soft shadows. Our main contribution is to approximate the Environment Map in a wavelet basis, keeping only the largest terms (this is known as a non-linear approximation). We obtain further compression by encoding the light transport matrix sparsely but accurately in the same basis. Rendering is performed by multiplying a sparse light vector by a sparse transport matrix, which is very fast. For accurate rendering, using non-linear wavelets is an order of magnitude faster than using linear spherical harmonics, the current best technique.
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frequency space Environment Map rendering
International Conference on Computer Graphics and Interactive Techniques, 2002Co-Authors: Ravi Ramamoorthi, Pat HanrahanAbstract:We present a new method for real-time rendering of objects with complex isotropic BRDFs under distant natural illumination, as specified by an Environment Map. Our approach is based on spherical frequency space analysis and includes three main contributions. Firstly, we are able to theoretically analyze required sampling rates and resolutions, which have traditionally been determined in an ad-hoc manner. We also introduce a new compact representation, which we call a spherical harmonic reflection Map (SHRM), for efficient representation and rendering. Finally, we show how to rapidly prefilter the Environment Map to compute the SHRM---our frequency domain prefiltering algorithm is generally orders of magnitude faster than previous angular (spatial) domain approaches.
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SIGGRAPH - Frequency space Environment Map rendering
Proceedings of the 29th annual conference on Computer graphics and interactive techniques - SIGGRAPH '02, 2002Co-Authors: Ravi Ramamoorthi, Pat HanrahanAbstract:We present a new method for real-time rendering of objects with complex isotropic BRDFs under distant natural illumination, as specified by an Environment Map. Our approach is based on spherical frequency space analysis and includes three main contributions. Firstly, we are able to theoretically analyze required sampling rates and resolutions, which have traditionally been determined in an ad-hoc manner. We also introduce a new compact representation, which we call a spherical harmonic reflection Map (SHRM), for efficient representation and rendering. Finally, we show how to rapidly prefilter the Environment Map to compute the SHRM---our frequency domain prefiltering algorithm is generally orders of magnitude faster than previous angular (spatial) domain approaches.
Ravi Ramamoorthi - One of the best experts on this subject based on the ideXlab platform.
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structured importance sampling of Environment Maps
International Conference on Computer Graphics and Interactive Techniques, 2003Co-Authors: Sameer Agarwal, Ravi Ramamoorthi, Serge Belongie, Henrik Wann JensenAbstract:We introduce structured importance sampling, a new technique for efficiently rendering scenes illuminated by distant natural illumination given in an Environment Map. Our method handles occlusion, high-frequency lighting, and is significantly faster than alternative methods based on Monte Carlo sampling. We achieve this speedup as a result of several ideas. First, we present a new metric for stratifying and sampling an Environment Map taking into account both the illumination intensity as well as the expected variance due to occlusion within the scene. We then present a novel hierarchical stratification algorithm that uses our metric to automatically stratify the Environment Map into regular strata. This approach enables a number of rendering optimizations, such as pre-integrating the illumination within each stratum to eliminate noise at the cost of adding bias, and sorting the strata to reduce the number of sample rays. We have rendered several scenes illuminated by natural lighting, and our results indicate that structured importance sampling is better than the best previous Monte Carlo techniques, requiring one to two orders of magnitude fewer samples for the same image quality.
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all frequency shadows using non linear wavelet lighting approximation
International Conference on Computer Graphics and Interactive Techniques, 2003Co-Authors: Ravi Ramamoorthi, Pat HanrahanAbstract:We present a method, based on pre-computed light transport, for real-time rendering of objects under all-frequency, time-varying illumination represented as a high-resolution Environment Map. Current techniques are limited to small area lights, with sharp shadows, or large low-frequency lights, with very soft shadows. Our main contribution is to approximate the Environment Map in a wavelet basis, keeping only the largest terms (this is known as a non-linear approximation). We obtain further compression by encoding the light transport matrix sparsely but accurately in the same basis. Rendering is performed by multiplying a sparse light vector by a sparse transport matrix, which is very fast. For accurate rendering, using non-linear wavelets is an order of magnitude faster than using linear spherical harmonics, the current best technique.
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frequency space Environment Map rendering
International Conference on Computer Graphics and Interactive Techniques, 2002Co-Authors: Ravi Ramamoorthi, Pat HanrahanAbstract:We present a new method for real-time rendering of objects with complex isotropic BRDFs under distant natural illumination, as specified by an Environment Map. Our approach is based on spherical frequency space analysis and includes three main contributions. Firstly, we are able to theoretically analyze required sampling rates and resolutions, which have traditionally been determined in an ad-hoc manner. We also introduce a new compact representation, which we call a spherical harmonic reflection Map (SHRM), for efficient representation and rendering. Finally, we show how to rapidly prefilter the Environment Map to compute the SHRM---our frequency domain prefiltering algorithm is generally orders of magnitude faster than previous angular (spatial) domain approaches.
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SIGGRAPH - Frequency space Environment Map rendering
Proceedings of the 29th annual conference on Computer graphics and interactive techniques - SIGGRAPH '02, 2002Co-Authors: Ravi Ramamoorthi, Pat HanrahanAbstract:We present a new method for real-time rendering of objects with complex isotropic BRDFs under distant natural illumination, as specified by an Environment Map. Our approach is based on spherical frequency space analysis and includes three main contributions. Firstly, we are able to theoretically analyze required sampling rates and resolutions, which have traditionally been determined in an ad-hoc manner. We also introduce a new compact representation, which we call a spherical harmonic reflection Map (SHRM), for efficient representation and rendering. Finally, we show how to rapidly prefilter the Environment Map to compute the SHRM---our frequency domain prefiltering algorithm is generally orders of magnitude faster than previous angular (spatial) domain approaches.
Nicholas M Patrikalakis - One of the best experts on this subject based on the ideXlab platform.
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Global motion planning under uncertain motion, sensing, and Environment Map
Autonomous Robots, 2012Co-Authors: Hanna Kurniawati, Tirthankar Bandyopadhyay, Nicholas M PatrikalakisAbstract:Uncertainty in motion planning is often caused by three main sources: motion error, sensing error, and imperfect Environment Map. Despite the significant effect of all three sources of uncertainty to motion planning problems, most planners take into account only one or at most two of them. We propose a new motion planner, called Guided Cluster Sampling (GCS) , that takes into account all three sources of uncertainty for robots with active sensing capabilities. GCS uses the Partially Observable Markov Decision Process (POMDP) framework and the point-based POMDP approach. Although point-based POMDPs have shown impressive progress over the past few years, it performs poorly when the Environment Map is imperfect. This poor performance is due to the extremely high dimensional state space, which translates to the extremely large belief space B . We alleviate this problem by constructing a more suitable sampling distribution based on the observations that when the robot has active sensing capability, B can be partitioned into a collection of much smaller sub-spaces, and an optimal policy can often be generated by sufficient sampling of a small subset of the collection. Utilizing these observations, GCS samples B in two-stages, a subspace is sampled from the collection and then a belief is sampled from the subspace. It uses information from the set of sampled sub-spaces and sampled beliefs to guide subsequent sampling. Simulation results on marine robotics scenarios suggest that GCS can generate reasonable policies for motion planning problems with uncertain motion, sensing, and Environment Map, that are unsolvable by the best point-based POMDPs today. Furthermore, GCS handles POMDPs with continuous state, action, and observation spaces. We show that for a class of POMDPs that often occur in robot motion planning, given enough time, GCS converges to the optimal policy. To the best of our knowledge, this is the first convergence result for point-based POMDPs with continuous action space.
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global motion planning under uncertain motion sensing and Environment Map
Robotics: Science and Systems, 2011Co-Authors: Hanna Kurniawati, Tirthankar Bandyopadhyay, Nicholas M PatrikalakisAbstract:Motion planning that takes into account uncertainty in motion, sensing, and Environment Map, is critical for autonomous robots to operate reliably in our living spaces. Partially Observable Markov Decision Processes (POMDPs) is a principled and general framework for planning under uncertainty. Although recent development of point-based POMDPs have drastically increased the speed of POMDP planning, even the best POMDP planner today, fails to generate reasonable motion strategies when the Environment Map is not known exactly. This paper presents Guided Cluster Sampling (GCS), a new point-based POMDP planner for motion planning with uncertain motion, sensing, and Environment Map, when the robot has active sensing capability. It uses our observations that in this problem, the belief space B can be partitioned into a collection of much smaller sub-spaces, and an optimal policy can often be generated by sufficient sampling of a small subset of the collection. GCS samples B using two-stage cluster sampling, a subspace is sampled from the collection and then a belief is sampled from the subspace. It uses information from the set of sampled sub-spaces and sampled beliefs to guide subsequent sampling. Preliminary results suggest that GCS generates reasonable policies for motion planning problems with uncertain motion, sensing, and Environment Map, that are unsolvable by the best point-based POMDP planner today, within reasonable time. Furthermore, GCS handles POMDPs with continuous state, action, and observation spaces. We show that for a class of POMDPs that often occur in robot motion planning, GCS converges to the optimal policy, given enough time. To the best of our knowledge, this is the first convergence result for point-based POMDPs with continuous action space.
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Robotics: Science and Systems - Global Motion Planning under Uncertain Motion, Sensing, and Environment Map
Robotics: Science and Systems VII, 2011Co-Authors: Hanna Kurniawati, Bandyopadhyay, Nicholas M PatrikalakisAbstract:Motion planning that takes into account uncertainty in motion, sensing, and Environment Map, is critical for autonomous robots to operate reliably in our living spaces. Partially Observable Markov Decision Processes (POMDPs) is a principled and general framework for planning under uncertainty. Although recent development of point-based POMDPs have drastically increased the speed of POMDP planning, even the best POMDP planner today, fails to generate reasonable motion strategies when the Environment Map is not known exactly. This paper presents Guided Cluster Sampling (GCS), a new point-based POMDP planner for motion planning with uncertain motion, sensing, and Environment Map, when the robot has active sensing capability. It uses our observations that in this problem, the belief space B can be partitioned into a collection of much smaller sub-spaces, and an optimal policy can often be generated by sufficient sampling of a small subset of the collection. GCS samples B using two-stage cluster sampling, a subspace is sampled from the collection and then a belief is sampled from the subspace. It uses information from the set of sampled sub-spaces and sampled beliefs to guide subsequent sampling. Preliminary results suggest that GCS generates reasonable policies for motion planning problems with uncertain motion, sensing, and Environment Map, that are unsolvable by the best point-based POMDP planner today, within reasonable time. Furthermore, GCS handles POMDPs with continuous state, action, and observation spaces. We show that for a class of POMDPs that often occur in robot motion planning, GCS converges to the optimal policy, given enough time. To the best of our knowledge, this is the first convergence result for point-based POMDPs with continuous action space.
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Global Motion Planning under Uncertain Motion, Sensing, and Environment Map
2011Co-Authors: Hanna Kurniawati, Bandyopadhyay, Nicholas M PatrikalakisAbstract:Uncertainty in motion planning is often caused by three main sources: motion error, sensing error, and imperfect Environment Map. Despite the significant effect of all three sources of uncertainty to motion planning problems, most planners take into account only one or at most two of them. We propose a new motion planner, called Guided Cluster Sampling (GCS), that takes into account all three sources of uncertainty for robots with active sensing capabilities. GCS uses the Partially Observable Markov Decision Process (POMDP) framework and the point-based POMDP approach. Although point-based POMDPs have shown impressive progress over the past few years, it performs poorly when the Environment Map is imperfect. This poor performance is due to the extremely high dimensional state space, which translates to the extremely large belief space B.
Tobias Höllerer - One of the best experts on this subject based on the ideXlab platform.
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Robust Relocalization and Its Evaluation for Online Environment Map Construction
IEEE transactions on visualization and computer graphics, 2010Co-Authors: Sehwan Kim, Christopher Coffin, Tobias HöllererAbstract:The acquisition of surround-view panoramas using a single hand-held or head-worn camera relies on robust real-time camera orientation tracking and relocalization. This paper presents robust methodology and evaluation for camera orientation relocalization, using virtual keyframes for online Environment Map construction. In the case of tracking loss, incoming camera frames are matched against known-orientation keyframes to re-estimate camera orientation. Instead of solely using real keyframes from incoming video, the proposed approach employs virtual keyframes which are distributed strategically within completed portions of an Environment Map. To improve tracking speed, we introduce a new variant of our system which carries out relocalization only when tracking fails and uses inexpensive image-patch descriptors. We compare different system variants using three evaluation methods to show that the proposed system is useful in a practical sense. To improve relocalization robustness against lighting changes in indoor and outdoor Environments, we propose a new approach based on illumination normalization and saturated area removal. We examine the performance of our solution over several indoor and outdoor video sequences, evaluating relocalization rates based on ground truth from a pan-tilt unit.
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relocalization using virtual keyframes for online Environment Map construction
Virtual Reality Software and Technology, 2009Co-Authors: Sehwan Kim, Christopher Coffin, Tobias HöllererAbstract:The acquisition of surround-view panoramas using a single hand-held or head-worn camera relies on robust real-time camera orientation tracking. In absence of robust tracking recovery methods, the complete acquisition process has to be re-started when tracking fails. This paper presents methodology for camera orientation relocalization, using virtual keyframes for online Environment Map construction. Instead of relying on real keyframes from incoming video, the proposed approach enables camera orientation relocalization by employing virtual keyframes which are distributed strategically within an Environment Map. We discuss our insights about a suitable number and distribution of virtual keyframes, as suggested by our experiments on virtual keyframe generation and orientation relocalization. After a shading correction step, we relocalize camera orientation in real-time by comparing the current camera frame to virtual keyframes. While expanding the captured Environment Map, we continue to simultaneously generate virtual keyframes within the completed portion of the Map, as descriptors to estimate camera orientation. We implemented our camera orientation relocalizer with the help of a GPU fragment shader for real-time application, and evaluated the speed and accuracy of the proposed approach.
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VRST - Relocalization using virtual keyframes for online Environment Map construction
Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology - VRST '09, 2009Co-Authors: Sehwan Kim, Christopher Coffin, Tobias HöllererAbstract:The acquisition of surround-view panoramas using a single hand-held or head-worn camera relies on robust real-time camera orientation tracking. In absence of robust tracking recovery methods, the complete acquisition process has to be re-started when tracking fails. This paper presents methodology for camera orientation relocalization, using virtual keyframes for online Environment Map construction. Instead of relying on real keyframes from incoming video, the proposed approach enables camera orientation relocalization by employing virtual keyframes which are distributed strategically within an Environment Map. We discuss our insights about a suitable number and distribution of virtual keyframes, as suggested by our experiments on virtual keyframe generation and orientation relocalization. After a shading correction step, we relocalize camera orientation in real-time by comparing the current camera frame to virtual keyframes. While expanding the captured Environment Map, we continue to simultaneously generate virtual keyframes within the completed portion of the Map, as descriptors to estimate camera orientation. We implemented our camera orientation relocalizer with the help of a GPU fragment shader for real-time application, and evaluated the speed and accuracy of the proposed approach.
Takeo Fujii - One of the best experts on this subject based on the ideXlab platform.
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ICOIN - Reliable vehicle-to-vehicle communication using spectrum Environment Map
2018 International Conference on Information Networking (ICOIN), 2018Co-Authors: Shunsuke Tsurumi, Takeo FujiiAbstract:Recently, the autonomous vehicle technologies, in which a vehicle has autonomous drive function without driver, have attracted attention. Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications can be used for cooperation among vehicles or between a roadside unit and vehicles for realizing cooperated autonomous vehicles. However, since vehicles always move, the quality of the received signals is fluctuated depending on the number of vehicles, the distance of the communicated vehicles, surrounding building structures, and geolocation. In this paper, we consider reliable V2V/V2I communications, which adaptively selects adequate parameters for improving communication efficiency by checking the surrounding Environment using the spectrum Environment Map. Here the spectrum Environment Map is generated by collected surrounding Environment information observed at distributed vehicles.
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Kriging-Based Interference Power Constraint: Integrated Design of the Radio Environment Map and Transmission Power
IEEE Transactions on Cognitive Communications and Networking, 2017Co-Authors: Koya Sato, Takeo FujiiAbstract:This paper proposes a probabilistic interference constraint method with a radio Environment Map (REM) for spatial spectrum sharing. The REM stores the spatial distribution of the average received signal power. We can optimize the accuracy of the measurement-based REM using the Kriging interpolation. Although several researchers have maintained a continuous interest in improving the accuracy of the REM, sufficient study has not been done to actually explore the interference constraint considering the estimation error. The proposed method uses ordinary Kriging interpolation for the spectrum cartography. According to the predicted distribution of the estimation error, the allowable interference power to the primary user is approximately formulated. Numerical results show that the proposed method can achieve the probabilistic interference constraint asymptotically. Additionally, we compare the performance of the proposed technique with three methods: the perfect estimation, the path loss-based method, and the Kriging-based method without the error prediction. The comparison results show that the proposed method has a higher spectrum sharing opportunity than the path loss-based method, even if only a small amount of measurement data is available. It is also shown that the proposed method dramatically improves the outage probability of the interference power compared to the conventional Kriging-based method.
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GLOBECOM Workshops - Kriging-Based Interference Power Constraint for Spectrum Sharing Based on Radio Environment Map
2015 IEEE Globecom Workshops (GC Wkshps), 2015Co-Authors: Koya Sato, Takeo FujiiAbstract:This paper proposes a probabilistic interference constraint method with radio Environment Map (REM) for spectrum sharing. Kriging interpolation is a well-known technique that can optimize the REM accuracy. However, although many researchers have maintained continuous interests in the accuracy of the REM, little study has been done to actually explore the interference constraint considering the estimation error. The proposed method uses ordinary kriging interpolation for the spectrum cartography. According to the predicted distribution of the estimation error, the allowed interference power to the primary user (PU) is approximately formulated. Numerical results show that the proposed method can achieve the probabilistic interference constraint asymptotically. Additionally, we compare the performance of the proposed technique with two methods: limit of the path loss-based method and perfect estimation. From the comparison results, it is shown that the kriging-based method has higher spectrum sharing opportunity than the path loss-based method, even if there are few measurement data.