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Found 35 papers, 14 papers with code
MoST: Multi-modality Scene Tokenization for Motion Prediction
This symbolic representation is a high-level abstraction of the real world, which may render the motion prediction model vulnerable to perception errors (e. g., failures in detecting open-vocabulary obstacles) while missing salient information from the scene context (e. g., poor road conditions).
Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving
Closed-set 3D perception models trained on only a pre-defined set of object categories can be inadequate for safety critical applications such as autonomous driving where new object types can be encountered after deployment.
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences
The MoDAR modality propagates object information from temporal contexts to a target frame, represented as a set of virtual points, one for each object from a waypoint on a forecasted trajectory.
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting
To study the effect of these modular approaches, design new paradigms that mitigate these limitations, and accelerate the development of end-to-end motion forecasting models, we augment the Waymo Open Motion Dataset (WOMD) with large-scale, high-quality, diverse LiDAR data for the motion forecasting task.
GINA-3D: Learning to Generate Implicit Neural Assets in the Wild
Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving.
NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors
Formulating single-view reconstruction as an image-conditioned 3D generation problem, we optimize the NeRF representations by minimizing a diffusion loss on its arbitrary view renderings with a pretrained image diffusion model under the input-view constraint.
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining
Continued improvements in deep learning architectures have steadily advanced the overall performance of 3D object detectors to levels on par with humans for certain tasks and datasets, where the overall performance is mostly driven by common examples.
Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving
Learning-based perception and prediction modules in modern autonomous driving systems typically rely on expensive human annotation and are designed to perceive only a handful of predefined object categories.
LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds
Our method co-designs an efficient labeling process with semi/weakly supervised learning and is applicable to nearly any 3D semantic segmentation backbones.
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds
Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving.
Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking
Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving.
RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding
Lidars are depth measuring sensors widely used in autonomous driving and augmented reality.
Multi-Class 3D Object Detection with Single-Class Supervision
While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost.
Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving
3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps, failure modes for LiDAR, relative location between the camera and LiDAR, and a high bar for estimation accuracy.
Revisiting 3D Object Detection From an Egocentric Perspective
Given the insight that SDE would benefit from more accurate geometry descriptions, we propose to represent objects as amodal contours, specifically amodal star-shaped polygons, and devise a simple model, StarPoly, to predict such contours.
Lidar Range Image Compression with Deep Delta Encoding
However, most prior work focus on the generic point cloud representation, neglecting the spatial patterns of the points from lidar range images.
SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation
On the Waymo Open Dataset and KITTI, SPG improves 3D detection results of these two methods across all categories.
Ranked #6 on
3D Object Detection
on KITTI Cars Easy
Offboard 3D Object Detection from Point Cloud Sequences
While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, such as using machines to automatically generate high-quality 3D labels.
Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
To this end, we select a suite of diverse datasets and tasks to measure the effect of unsupervised pre-training on a large source set of 3D scenes.
Object-Centric Multi-View Aggregation
We present an approach for aggregating a sparse set of views of an object in order to compute a semi-implicit 3D representation in the form of a volumetric feature grid.
ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes
Compared to prior work on multi-modal detection, we explicitly extract both geometric and semantic features from the 2D images.
Ranked #2 on
3D Object Detection
on SUN-RGBD
(using extra training data)
Deep Hough Voting for 3D Object Detection in Point Clouds
Current 3D object detection methods are heavily influenced by 2D detectors.
3D Object Detection
3D Object Detection From Monocular Images
+2
KPConv: Flexible and Deformable Convolution for Point Clouds
Furthermore, these locations are continuous in space and can be learned by the network.
Ranked #1 on
3D Semantic Segmentation
on DALES
Generating 3D Adversarial Point Clouds
Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions.
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks
The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks.
FlowNet3D: Learning Scene Flow in 3D Point Clouds
In this work, we propose a novel deep neural network named $FlowNet3D$ that learns scene flow from point clouds in an end-to-end fashion.
Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks
The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks.
Exploring the Hidden Dimension in Accelerating Convolutional Neural Networks
DeePa is a deep learning framework that explores parallelism in all parallelizable dimensions to accelerate the training process of convolutional neural networks.
Frustum PointNets for 3D Object Detection from RGB-D Data
In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.
Ranked #1 on
Object Localization
on KITTI Pedestrians Easy
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Ranked #2 on
Semantic Segmentation
on Toronto-3D L002
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Point cloud is an important type of geometric data structure.
Ranked #1 on
3D Face Reconstruction
on !(()&&!|*|*|
FPNN: Field Probing Neural Networks for 3D Data
Each field probing filter is a set of probing points --- sensors that perceive the space.
Ranked #5 on
3D Object Recognition
on ModelNet40
Volumetric and Multi-View CNNs for Object Classification on 3D Data
Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations.
Ranked #3 on
3D Object Recognition
on ModelNet40
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
Object viewpoint estimation from 2D images is an essential task in computer vision.
