This paper investigates the benefits of incorporating point visibility information of 3D point clouds within a deep learning framework, using occlusion-aware 3D priors. The presented methods for deriving the visibility of each point rely on ray-casting techniques, making the proposed solution generic and sensor independent. We demonstrate the benefits of integrating point visibility using two real-world applications. In a first application, a novel data augmentation technique is proposed leveraging occlusion-aware CAD 3D priors, resulting in state-of-the-art 3D vehicle detection. In a second application, we integrate visibility information into a vehicle pose estimation pipeline based on 3D priors. The presented techniques achieve state-of-the-art performance, significantly improving both translation and rotation accuracy on the Apollo3DCar dataset.

cREAtIve targets the development of novel highly-adaptable embedded deep learning solutions for automotive and traffic monitoring applications, including position sensor processing, scene interpretation based on LiDAR, and object detection and classification in thermal images for traffic camera systems. These applications share the need for deep learning solutions tailored for deployment on embedded devices with limited resources and featuring high adaptability and robustness to changing environmental conditions. cREAtIve develops knowledge, tools and methods that enable hardware-efficient, adaptable, and robust deep learning.

Abstract This paper presents a novel wavelet‐based transform and coding scheme for irregular meshes. The transform preserves geometric features at lower resolutions by adaptive vertex sampling and retriangulation, resulting in more accurate subsampling and better avoidance of smoothing and aliasing artefacts. By employing octree‐based coding techniques, the encoding of both connectivity and geometry information is decoupled from any mesh traversal order, and allows for exploiting the intra‐band statistical dependencies between wavelet coefficients. Improvements over the state of the art obtained by our approach are three‐fold: (1) improved rate–distortion performance over Wavemesh and IPR for both the Hausdorff and root mean square distances at low‐to‐mid‐range bitrates, most obvious when clear geometric features are present while remaining competitive for smooth, feature‐poor models; (2) improved rendering performance at any triangle budget, translating to a better quality for the same runtime memory footprint; (3) improved visual quality when applying similar limits to the bitrate or triangle budget, showing more pronounced improvements than rate–distortion curves.

Computation-intensive image processing applications need to be implemented on multicore architectures. If they are to be executed efficiently on such platforms, the underlying data and/or functions should be partitioned and distributed among the processors. The optimal partitioning approach is the one which aims to minimize the inter-processor communication while maximizing the load balance. With the continuously increasing number of cores which exacerbates the demand for more complex memory hierarchies, non-uniform memory access, etc., on-chip communication has gained a significant role in taking advantage of the multicore chips. Therefore, making partitioning decisions just based on conventional performance results and without communication profiling is suboptimal. In this paper, we explore the behavior of a mesh decoder as a case study in terms of communication and computation, and propose models that allow early prediction of the application's behavior. Using these models, profiling the application for all of the input samples is not necessary anymore. As a result, communication- and computation-aware parallelization could be performed faster and easier.

In this paper we present a remeshing algorithm which drastically reduces the aliasing artifacts inherent in regularly-sampled remeshed objects. Starting from a semi-regular mesh, the proposed algorithm reduces the remeshing error and avoids aliasing by displacing vertices such that most samples of the original mesh are present in the remeshed model as well. Computational efficiency is provided by using a search-tree, which efficiently gathers vertices near a given point in a 3D space. Compared to the state-of-the-art semi-regular remesher, the proposed remesher drastically improves the visual quality of the high-frequency regions in remeshed objects. Additionally, the proposed algorithm yields a lower remeshing error, which is reflected by a significantly increased PSNR upper-bound in wavelet-based compression of such meshes.

This paper proposes novel scalable mesh coding designs exploiting the intraband or composite statistical dependencies between the wavelet coefficients. A Laplacian mixture model is proposed to approximate the distribution of the wavelet coefficients. This model proves to be more accurate when compared to commonly employed single Laplacian or generalized Gaussian distribution models. Using the mixture model, we determine theoretically the optimal embedded quantizers to be used in scalable wavelet-based coding of semiregular meshes. In this sense, it is shown that the commonly employed successive approximation quantization is an acceptable, but in general, not an optimal solution. Novel scalable intraband and composite mesh coding systems are proposed, following an information-theoretic analysis of the statistical dependencies between the coefficients. The wavelet subbands are independently encoded using octree-based coding techniques. Furthermore, context-based entropy coding employing either intraband or composite models is applied. The proposed codecs provide both resolution and quality scalability. This lies in contrast to the state-of-the-art interband zerotree-based semiregular mesh coding technique, which supports only quality scalability. Additionally, the experimental results show that, on average, the proposed codecs outperform the interband state-of-the-art for both normal and nonnormal meshes. Finally, compared with a zerotree coding system, the proposed coding schemes are better suited for software/hardware parallelism, due to the independent processing of wavelet subbands.

In this paper, a novel wavelet-based composite mesh coding scheme is presented. In contrast to the state-of-the-art scalable interband mesh codec, the proposed codec relies on composite dependency models that capture both the interband and intraband statistical dependencies between wavelet coefficients. Additionally, each wavelet subband is processed independently, which allows for parallelized processing and for a progressive reconstruction of each mesh resolution level. Compared to the state-of-the-art, the proposed codec yields for almost all rate points superior compression performance in L2-sense. Furthermore, the generated bitstreams are near-optimal in rate-distortion sense, which eliminates the need of a post-compression rate-distortion optimization technique.