Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Deforming Scene from Monocular Video

In this tech report, we present the current state of our ongoing work on reconstructing Neural Radiance Fields (NERF) of general non-rigid scenes via ray bending. Non-rigid NeRF (NR-NeRF) takes RGB images of a deforming object (e.g., from a monocular video) as input and then learns a geometry and appearance representation that not only allows to reconstruct the input sequence but also to re-render any time step into novel camera views with high fidelity. In particular, we show that a consumer-grade camera is sufficient to synthesize convincing bullet-time videos of short and simple scenes. In addition, the resulting representation enables correspondence estimation across views and time, and provides rigidity scores for each point in the scene. We urge the reader to watch the supplemental videos for qualitative results. We will release our code

QuAnt: Quantum Annealing with Learnt Couplings

Modern quantum annealers can find high-quality solutions to combinatorialoptimisation objectives given as quadratic unconstrained binary optimisation(QUBO) problems. Unfortunately, obtaining suitable QUBO forms in computervision remains challenging and currently requires problem-specific analyticalderivations. Moreover, such explicit formulations impose tangible constraintson solution encodings. In stark contrast to prior work, this paper proposes tolearn QUBO forms from data through gradient backpropagation instead of derivingthem. As a result, the solution encodings can be chosen flexibly and compactly.Furthermore, our methodology is general and virtually independent of thespecifics of the target problem type. We demonstrate the advantages of learntQUBOs on the diverse problem types of graph matching, 2D point cloud alignmentand 3D rotation estimation. Our results are competitive with the previousquantum state of the art while requiring much fewer logical and physicalqubits, enabling our method to scale to larger problems. The code and the newdataset will be open-sourced.<br

State of the Art in Dense Monocular Non-Rigid 3D Reconstruction

3D reconstruction of deformable (or non-rigid) scenes from a set of monocular2D image observations is a long-standing and actively researched area ofcomputer vision and graphics. It is an ill-posed inverse problem,since--without additional prior assumptions--it permits infinitely manysolutions leading to accurate projection to the input 2D images. Non-rigidreconstruction is a foundational building block for downstream applicationslike robotics, AR/VR, or visual content creation. The key advantage of usingmonocular cameras is their omnipresence and availability to the end users aswell as their ease of use compared to more sophisticated camera set-ups such asstereo or multi-view systems. This survey focuses on state-of-the-art methodsfor dense non-rigid 3D reconstruction of various deformable objects andcomposite scenes from monocular videos or sets of monocular views. It reviewsthe fundamentals of 3D reconstruction and deformation modeling from 2D imageobservations. We then start from general methods--that handle arbitrary scenesand make only a few prior assumptions--and proceed towards techniques makingstronger assumptions about the observed objects and types of deformations (e.g.human faces, bodies, hands, and animals). A significant part of this STAR isalso devoted to classification and a high-level comparison of the methods, aswell as an overview of the datasets for training and evaluation of thediscussed techniques. We conclude by discussing open challenges in the fieldand the social aspects associated with the usage of the reviewed methods.<br

Automotive shredder residue (ASR) characterization for a valuable management

Car fluff is the waste produced after end-of-life-vehicles (ELVs) shredding and metal recovery. It is made of plastics, rubber, glass, textiles and residual metals and it accounts for almost one-third of a vehicle mass. Due to the approaching of Directive 2000/53/EC recycling targets, 85% recycling rate and 95% recovery rate in 2015, the implementation of automotive shredder residue (ASR) sorting and recycling technologies appears strategic. The present work deals with the characterization of the shredder residue coming from an industrial plant, representative of the Italian situation, as for annual fluxes and technologies involved. The aim of this study is to characterize ASR in order to study and develop a cost effective and environmentally sustainable recycling system. Results show that almost half of the residue is made of fines and the remaining part is mainly composed of polymers. Fine fraction is the most contaminated by mineral oils and heavy metals. This fraction produces also up to 40% ashes and its LHV is lower than the plastic-rich one. Foam rubber represents around half of the polymers share in car fluff. Moreover, some chemical\u2013physical parameters exceed the limits of some parameters fixed by law to be considered refuse derived fuel (RDF). As a consequence, ASR needs to be pre-treated in order to follow the energy recovery route