Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

happiness

,Learning to?Generate Realistic LiDAR Point Clouds, approach produces more realistic samples than other generative models. Furthermore, LiDARGen can sample point clouds conditioned on inputs without retraining. We demonstrate that our proposed generative model could be directly used to densify LiDAR point clouds. Our code is available at: ..

Ischemic-Stroke

,Spatially Invariant Unsupervised 3D Object-Centric Learning and?Scene Decomposition, with an arbitrary number of objects. We evaluate our method on the task of unsupervised scene decomposition. Experimental results demonstrate that . has strong scalability and is capable of detecting and segmenting an unknown number of objects from a point cloud in an unsupervised manner.

intricacy

bioavailability

,Learning to?Generate Realistic LiDAR Point Clouds,rages the powerful score-matching energy-based model and formulates the point cloud generation process as a stochastic denoising process in the equirectangular view. This model allows us to sample diverse and high-quality point cloud samples with guaranteed physical feasibility and controllability.

聾子

,RFNet-4D: Joint Object Reconstruction and?Flow Estimation from?4D Point Clouds,nstruction from time-varying point clouds (a.k.a. 4D point clouds) is generally overlooked. In this paper, we propose a new network architecture, namely RFNet-4D, that jointly reconstruct objects and their motion flows from 4D point clouds. The key insight is that simultaneously performing both task

琺瑯

,Diverse Image Inpainting with?Normalizing Flow,he “corrupted region" content consistent with the background and generate a variety of reasonable texture details. However, existing one-stage methods that directly output the inpainting results have to make a trade-off between diversity and consistency. The two-stage methods as the current trend ca

jettison

,Improved Masked Image Generation with?Token-Critic, their autoregressive counterparts. However, optimal parallel sampling from the true joint distribution of visual tokens remains an open challenge. In this paper we introduce Token-Critic, an auxiliary model to guide the sampling of a non-autoregressive generative transformer. Given a masked-and-rec

和平

,TREND: Truncated Generalized Normal Density Estimation of?Inception Embeddings for?GAN Evaluation,butions of the set of ground truth images and the set of generated test images. The Frechét Inception distance is one of the most widely used metrics for evaluation of GANs, which assumes that the features from a trained Inception model for a set of images follow a normal distribution. In this paper

鞭子

,Exploring Gradient-Based Multi-directional Controls in?GANs, the structure of the latent space in GANs largely remains as a black-box, leaving its controllable generation an open problem, especially when spurious correlations between different semantic attributes exist in the image distributions. To address this problem, previous methods typically learn line

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