Efficient Depth Image Reconstruction using Accelerated Proximal Gradient Method

Nguyen, Mau Uyen; Dao, Thanh Tinh; Tang, Van Ha

doi:10.1109/kse.2018.8573361

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…For the 4 × 4 sensing pattern of 16 depth pixels, two major metrics are considered: throughput with reconstructed depth pixels per second and energy consumption with micro Joules per reconstructed depth pixel. By considering the latency time of processing a single sensing block (4 × 4 pixel block), the system throughput in pixels per second is Throughput = 16/Latency, (12) while the energy of pixel processing in µJ per pixel is Energy = (Latency • Dynamic Power)/16.…”

Section: Efficiency Evaluationmentioning

confidence: 99%

“…Using full precision, researchers have explored the use of more efficient algorithms for compressive depth reconstruction using convex optimisation with algorithms such as the parallel alternating direction method of multipliers (ADMM) [5] and accelerated proximal gradient descent (PGD) [12] for depth reconstruction. Several authors have investigated the use of RP for convex optimization.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficient Approximate 3D Image Reconstruction

Aßmann

Stewart

et al. 2022

IEEE Trans. Emerg. Topics Comput.

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We demonstrate an efficient and accelerated parallel, sparse depth reconstruction framework using compressed sensing (CS) and approximate computing. Employing data parallelism for rapid image formation, the depth image is reconstructed from sparsely sampled scenes using convex optimization. Coupled with faster imaging, this sparse sampling reduces significantly the projected laser power in active systems such as LiDAR to allow eye safe operation at longer range. We also demonstrate how reduced precision is leveraged to reduce the number of logic units in FPGA implementations for such sparse imaging systems. It enables significant reduction in logic units, memory requirements and power consumption by over 80% with minimal impact on the quality of reconstruction. To further accelerate processing, pre-computed, important components of the lower-upper (LU) decomposition and other linear algebraic computations are used to solve the convex optimization problems. Our methodology is demonstrated by the application of the alternating direction method of multipliers (ADMM) and proximal gradient descent (PGD) algorithms. For comparison, a fully discrete least square reconstruction method (dSparse) is also presented. This demonstrates the feasibility of novel, high resolution, low power and high frame rate LiDAR depth imagers based on sparse illumination for use in applications where resources are strictly limited.

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Section: Efficiency Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Efficient Approximate 3D Image Reconstruction

Aßmann

Stewart

et al. 2022

IEEE Trans. Emerg. Topics Comput.

View full text Add to dashboard Cite

show abstract

Truncated γ norm-based low-rank and sparse decomposition

Yang

Lü

2022

Multimed Tools Appl

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Efficient Reconfigurable Mixed Precision $$\ell _1$$ Solver for Compressive Depth Reconstruction

Wallace

Mota

et al. 2022

J Sign Process Syst

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Rapid reconstruction of depth images from sparsely sampled data is important for many applications in machine perception, including robot or vehicle assistance or autonomy. Approximate computing techniques have been widely adopted to reduce resource consumption and increase efficiency in energy and resource constrained systems, especially targeted at FPGA and solid state implementation. Whereas previous work has focused on approximate, but static, representation of data in LiDAR systems, in this paper we show how the flexibility of an arbitrary precision accelerator with fine-grain tuning allows a better trade-off between algorithmic performance and implementation efficiency. A mixed precision framework of $$\ell _1$$ ℓ 1 solvers is presented, with compact ADMM and PGD, for the lasso problem, enabling compressive depth reconstruction by varying the precision scaling in single bit granularity during the iterative optimization process. Implementing mixed precision $$\ell _1$$ ℓ 1 solvers on an FPGA with a pipelined architecture for depth image reconstruction across various sensing scenarios, over 74% savings in hardware resources and 60% in power are achieved with only minor reductions in reconstructed depth image quality when compared to single float precision, while over 10% saving in hardware resources and power is achieved compared to relative consistently reduced precision solutions.

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Efficient Depth Image Reconstruction using Accelerated Proximal Gradient Method

Cited by 4 publications

References 19 publications

Energy Efficient Approximate 3D Image Reconstruction

Energy Efficient Approximate 3D Image Reconstruction

Truncated γ norm-based low-rank and sparse decomposition

Efficient Reconfigurable Mixed Precision $$\ell _1$$ Solver for Compressive Depth Reconstruction

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