point cloud


Self-Supervised Deep Learning on Point Clouds by Reconstructing Space

Neural Information Processing Systems

Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising results on supervised learning tasks such as object classification and semantic segmentation. While massive point cloud datasets can be captured using modern scanning technology, manually labelling such large 3D point clouds for supervised learning tasks is a cumbersome process. This necessitates methods that can learn from unlabelled data to significantly reduce the number of annotated samples needed in supervised learning. We propose a self-supervised learning task for deep learning on raw point cloud data in which a neural network is trained to reconstruct point clouds whose parts have been randomly rearranged.


Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

Neural Information Processing Systems

We propose a novel, conceptually simple and general framework for instance segmentation on 3D point clouds. Our method, called 3D-BoNet, follows the simple design philosophy of per-point multilayer perceptrons (MLPs). It consists of a backbone network followed by two parallel network branches for 1) bounding box regression and 2) point mask prediction. Moreover, it is remarkably computationally efficient as, unlike existing approaches, it does not require any post-processing steps such as non-maximum suppression, feature sampling, clustering or voting. Extensive experiments show that our approach surpasses existing work on both ScanNet and S3DIS datasets while being approximately 10x more computationally efficient.


Controlling Neural Level Sets

Neural Information Processing Systems

The level sets of neural networks represent fundamental properties such as decision boundaries of classifiers and are used to model non-linear manifold data such as curves and surfaces. Thus, methods for controlling the neural level sets could find many applications in machine learning. In this paper we present a simple and scalable approach to directly control level sets of a deep neural network. Our method consists of two parts: (i) sampling of the neural level sets, and (ii) relating the samples' positions to the network parameters. The latter is achieved by a sample network that is constructed by adding a single fixed linear layer to the original network.


Camera-Lidar Projection: Navigating between 2D and 3D

#artificialintelligence

Lidars and cameras are two essential sensors for perception and scene understanding. They build an environment in tandem and provide a means for detection and localisation of other objects, giving robots rich semantic information required for safe navigation. Many researchers have started exploring multi-modal deep learning model for precise 3D object detection. An interesting example would be an algorithm developed by Aptiv, PointPainting[1] Camera outperforms LIDAR when it comes to capturing denser and richer representation. From fig 2, looking at the sparse point cloud alone, it is relatively difficult to correctly identify the black box as a pedestrian.


mmFall: Fall Detection using 4D MmWave Radar and Variational Recurrent Autoencoder

arXiv.org Machine Learning

In this paper we propose mmFall - a novel fall detection system, which comprises of (i) the emerging millimeter-wave (mmWave) radar sensor to collect the human body's point cloud along with the body centroid, and (ii) a variational recurrent autoencoder (VRAE) to compute the anomaly level of the body motion based on the acquired point cloud. A fall is claimed to have occurred when the spike in anomaly level and the drop in centroid height occur simultaneously. The mmWave radar sensor provides several advantages, such as privacycompliance and high-sensitivity to motion, over the traditional sensing modalities. However, (i) randomness in radar point cloud data and (ii) difficulties in fall collection/labeling in the traditional supervised fall detection approaches are the two main challenges. To overcome the randomness in radar data, the proposed VRAE uses variational inference, a probabilistic approach rather than the traditional deterministic approach, to infer the posterior probability of the body's latent motion state at each frame, followed by a recurrent neural network (RNN) to learn the temporal features of the motion over multiple frames. Moreover, to circumvent the difficulties in fall data collection/labeling, the VRAE is built upon an autoencoder architecture in a semi-supervised approach, and trained on only normal activities of daily living (ADL) such that in the inference stage the VRAE will generate a spike in the anomaly level once an abnormal motion, such as fall, occurs. During the experiment, we implemented the VRAE along with two other baselines, and tested on the dataset collected in an apartment. The receiver operating characteristic (ROC) curve indicates that our proposed model outperforms the other two baselines, and achieves 98% detection out of 50 falls at the expense of just 2 false alarms.


IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning

arXiv.org Machine Learning

Medicine is an important application area for deep learning models. Research in this field is a combination of medical expertise and data science knowledge. In this paper, instead of 2D medical images, we introduce an open-access 3D intracranial aneurysm dataset, IntrA, that makes the application of points-based and mesh-based classification and segmentation models available. Our dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction. We provide a large-scale benchmark of classification and part segmentation by testing state-of-the-art networks. We also discuss the performance of each method and demonstrate the challenges of our dataset. The published dataset can be accessed here: https://github.com/intra3d2019/IntrA.


HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection

arXiv.org Artificial Intelligence

We present Hybrid Voxel Network (HVNet), a novel one-stage unified network for point cloud based 3D object detection for autonomous driving. Recent studies show that 2D voxelization with per voxel PointNet style feature extractor leads to accurate and efficient detector for large 3D scenes. Since the size of the feature map determines the computation and memory cost, the size of the voxel becomes a parameter that is hard to balance. A smaller voxel size gives a better performance, especially for small objects, but a longer inference time. A larger voxel can cover the same area with a smaller feature map, but fails to capture intricate features and accurate location for smaller objects. We present a Hybrid Voxel network that solves this problem by fusing voxel feature encoder (VFE) of different scales at point-wise level and project into multiple pseudo-image feature maps. We further propose an attentive voxel feature encoding that outperforms plain VFE and a feature fusion pyramid network to aggregate multi-scale information at feature map level. Experiments on the KITTI benchmark show that a single HVNet achieves the best mAP among all existing methods with a real time inference speed of 31Hz.


On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks

arXiv.org Machine Learning

While deep learning in 3D domain has achieved revolutionary performance in many tasks, the robustness of these models has not been sufficiently studied or explored. Regarding the 3D adversarial samples, most existing works focus on manipulation of local points, which may fail to invoke the global geometry properties, like robustness under linear projection that preserves the Euclidean distance, i.e., isometry. In this work, we show that existing state-of-the-art deep 3D models are extremely vulnerable to isometry transformations. Armed with the Thompson Sampling, we develop a black-box attack with success rate over 95\% on ModelNet40 data set. Incorporating with the Restricted Isometry Property, we propose a novel framework of white-box attack on top of spectral norm based perturbation. In contrast to previous works, our adversarial samples are experimentally shown to be strongly transferable. Evaluated on a sequence of prevailing 3D models, our white-box attack achieves success rates from 98.88\% to 100\%. It maintains a successful attack rate over 95\% even within an imperceptible rotation range $[\pm 2.81^{\circ}]$.


Triangle-Net: Towards Robustness in Point Cloud Classification

arXiv.org Machine Learning

3D object recognition is becoming a key desired capability for many computer vision systems such as autonomous vehicles, service robots and surveillance drones to operate more effectively in unstructured environments. These real-time systems require effective classification methods that are robust to sampling resolution, measurement noise, and pose configuration of the objects. Previous research has shown that sparsity, rotation and positional variance of points can lead to a significant drop in the performance of point cloud based classification techniques. In this regard, we propose a novel approach for 3D classification that takes sparse point clouds as input and learns a model that is robust to rotational and positional variance as well as point sparsity. To this end, we introduce new feature descriptors which are fed as an input to our proposed neural network in order to learn a robust latent representation of the 3D object. We show that such latent representations can significantly improve the performance of object classification and retrieval. Further, we show that our approach outperforms PointNet and 3DmFV by 34.4% and 27.4% respectively in classification tasks using sparse point clouds of only 16 points under arbitrary SO(3) rotation.


Topologically sensitive metaheuristics

arXiv.org Artificial Intelligence

We present the conceptual design of two topologically sensitive metaheuristics: 1. Topologically Sensitive Variable neighborhood search (TVNS) and 2. Topologically Sensitive Electromagnetism metaheuristics (TEM). Our intention is to show that this topological enhancement can be done in general case, therefore, we select two complementary techniques: VNS is single-solution based and discrete coded metaheuristic, while EM populationbased and real coded metaheuristic. The usability of such metaheuristics and their theoretical aspects will be discussed in further papers.