Deformable Model Retrieval Based on Topological and Geometric Signatures

With the increasing popularity of 3D applications such as computer games, a lot of 3D geometry models are being created. To encourage sharing and reuse, techniques that support matching and retrieval of these models are emerging. However, only a few of them can handle deformable models, i.e., models of different poses, and these methods are generally very slow. In this paper, we present a novel method for efficient matching and retrieval of 3D deformable models. Our research idea stresses on using both topological and geometric features at the same time. First, we propose Topological Point Ring (TPR) analysis to locate reliable topological points and rings. Second, we capture both local and global geometric information to characterize each of these topological features. To compare the similarity of two models, we adapt the Earth Mover Distance (EMD) as the distance function, and construct an indexing tree to accelerate the retrieval process. We demonstrate the performance of the new method, both in terms of accuracy and speed, through a large number of experiments

Egocentric Hand Detection Via Dynamic Region Growing

Egocentric videos, which mainly record the activities carried out by the users of the wearable cameras, have drawn much research attentions in recent years. Due to its lengthy content, a large number of ego-related applications have been developed to abstract the captured videos. As the users are accustomed to interacting with the target objects using their own hands while their hands usually appear within their visual fields during the interaction, an egocentric hand detection step is involved in tasks like gesture recognition, action recognition and social interaction understanding. In this work, we propose a dynamic region growing approach for hand region detection in egocentric videos, by jointly considering hand-related motion and egocentric cues. We first determine seed regions that most likely belong to the hand, by analyzing the motion patterns across successive frames. The hand regions can then be located by extending from the seed regions, according to the scores computed for the adjacent superpixels. These scores are derived from four egocentric cues: contrast, location, position consistency and appearance continuity. We discuss how to apply the proposed method in real-life scenarios, where multiple hands irregularly appear and disappear from the videos. Experimental results on public datasets show that the proposed method achieves superior performance compared with the state-of-the-art methods, especially in complicated scenarios

Lighting up NeRF via Unsupervised Decomposition and Enhancement

Neural Radiance Field (NeRF) is a promising approach for synthesizing novel views, given a set of images and the corresponding camera poses of a scene. However, images photographed from a low-light scene can hardly be used to train a NeRF model to produce high-quality results, due to their low pixel intensities, heavy noise, and color distortion. Combining existing low-light image enhancement methods with NeRF methods also does not work well due to the view inconsistency caused by the individual 2D enhancement process. In this paper, we propose a novel approach, called Low-Light NeRF (or LLNeRF), to enhance the scene representation and synthesize normal-light novel views directly from sRGB low-light images in an unsupervised manner. The core of our approach is a decomposition of radiance field learning, which allows us to enhance the illumination, reduce noise and correct the distorted colors jointly with the NeRF optimization process. Our method is able to produce novel view images with proper lighting and vivid colors and details, given a collection of camera-finished low dynamic range (8-bits/channel) images from a low-light scene. Experiments demonstrate that our method outperforms existing low-light enhancement methods and NeRF methods.Comment: ICCV 2023. Project website: https://whyy.site/paper/llner

CyberWalk : a web-based distributed virtual walkthrough environment

A distributed virtual walkthrough environment allows users connected to the geometry server to walk through a specific place of interest, without having to travel physically. This place of interest may be a virtual museum, virtual library or virtual university. There are two basic approaches to distribute the virtual environment from the geometry server to the clients, complete replication and on-demand transmission. Although the on-demand transmission approach saves waiting time and optimizes network usage, many technical issues need to be addressed in order for the system to be interactive. CyberWalk is a web-based distributed virtual walkthrough system developed based on the on-demand transmission approach. It achieves the necessary performance with a multiresolution caching mechanism. First, it reduces the model transmission and rendering times by employing a progressive multiresolution modeling technique. Second, it reduces the Internet response time by providing a caching and prefetching mechanism. Third, it allows a client to continue to operate, at least partially, when the Internet is disconnected. The caching mechanism of CyberWalk tries to maintain at least a minimum resolution of the object models in order to provide at least a coarse view of the objects to the viewer. All these features allow CyberWalk to provide sufficient interactivity to the user for virtual walkthrough over the Internet environment. In this paper, we demonstrate the design and implementation of CyberWalk. We investigate the effectiveness of the multiresolution caching mechanism of CyberWalk in supporting virtual walkthrough applications in the Internet environment through numerous experiments, both on the simulation system and on the prototype system

Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories

In this paper, we present a novel method to recognize the types of crowd movement from crowd trajectories using agent-based motion models (AMMs). Our idea is to apply a number of AMMs, referred to as exemplar-AMMs, to describe the crowd movement. Specifically, we propose an optimization framework that filters out the unknown noise in the crowd trajectories and measures their similarity to the exemplar-AMMs to produce a crowd motion feature. We then address our real-world crowd movement recognition problem as a multi-label classification problem. Our experiments show that the proposed feature outperforms the state-of-the-art methods in recognizing both simulated and real-world crowd movements from their trajectories. Finally, we have created a synthetic dataset, SynCrowd, which contains 2D crowd trajectories in various scenarios, generated by various crowd simulators. This dataset can serve as a training set or benchmark for crowd analysis work

Deformable Object Tracking with Gated Fusion

The tracking-by-detection framework receives growing attentions through the integration with the Convolutional Neural Networks (CNNs). Existing tracking-by-detection based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. Extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against state-of-the-art methods