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06/05/2023
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences
Occluded and long-range objects are ubiquitous and challenging for 3D ob...
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04/04/2023
GINA-3D: Learning to Generate Implicit Neural Assets in the Wild
Modeling the 3D world from sensor data for simulation is a scalable way ...
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12/06/2022
NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors
2D-to-3D reconstruction is an ill-posed problem, yet humans are good at ...
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10/15/2022
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining
Continued improvements in deep learning architectures have steadily adva...
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10/14/2022
LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds
Semantic segmentation of LiDAR point clouds is an important task in auto...
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10/14/2022
Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving
Learning-based perception and prediction modules in modern autonomous dr...
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10/10/2022
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds
Developing neural models that accurately understand objects in 3D point ...
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06/08/2022
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 i...
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06/02/2022
RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding
Lidars are depth measuring sensors widely used in autonomous driving and...
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05/11/2022
Multi-Class 3D Object Detection with Single-Class Supervision
While multi-class 3D detectors are needed in many robotics applications,...
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12/14/2021
Revisiting 3D Object Detection From an Egocentric Perspective
3D object detection is a key module for safety-critical robotics applica...
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08/15/2021
SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation
In autonomous driving, a LiDAR-based object detector should perform reli...
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03/08/2021
Offboard 3D Object Detection from Point Cloud Sequences
While current 3D object recognition research mostly focuses on the real-...
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07/21/2020
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
Arguably one of the top success stories of deep learning is transfer lea...
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07/20/2020
Object-Centric Multi-View Aggregation
We present an approach for aggregating a sparse set of views of an objec...
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01/29/2020
ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes
3D object detection has seen quick progress thanks to advances in deep l...
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04/21/2019
Deep Hough Voting for 3D Object Detection in Point Clouds
Current 3D object detection methods are heavily influenced by 2D detecto...
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04/18/2019
KPConv: Flexible and Deformable Convolution for Point Clouds
We present Kernel Point Convolution (KPConv), a new design of point conv...
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09/19/2018
Generating 3D Adversarial Point Clouds
Machine learning models especially deep neural networks (DNNs) have been...
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06/04/2018
Learning Scene Flow in 3D Point Clouds
Many applications in robotics and human-computer interaction can benefit...
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02/14/2018
Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks
The past few years have witnessed growth in the size and computational r...
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11/22/2017
Frustum PointNets for 3D Object Detection from RGB-D Data
While object recognition on 2D images is getting more and more mature, 3...
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06/07/2017
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Few prior works study deep learning on point sets. PointNet by Qi et al....
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12/02/2016
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Point cloud is an important type of geometric data structure. Due to its...
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05/20/2016
FPNN: Field Probing Neural Networks for 3D Data
Building discriminative representations for 3D data has been an importan...
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04/12/2016