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Found 10 papers, 5 papers with code
Generalizable Neural Human Renderer
While recent advancements in animatable human rendering have achieved remarkable results, they require test-time optimization for each subject which can be a significant limitation for real-world applications.
Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras
In order to evaluate our proposed pipeline, we collect three video sets of RGBD videos recorded from multiple sparse-view depth cameras and ground truth 3D poses are manually annotated.
Flexible Depth Completion for Sparse and Varying Point Densities
While recent depth completion methods have achieved remarkable results filling in relatively dense depth maps (e. g. projected 64-line LiDAR on KITTI or 500 sampled points on NYUv2) with RGB guidance their performance on very sparse input (e. g. 4-line LiDAR or 32 depth point measurements) is unverified.
F-RDW: Redirected Walking with Forecasting Future Position
In order to serve better VR experiences to users, existing predictive methods of Redirected Walking (RDW) exploit future information to reduce the number of reset occurrences.
Azimuth Super-Resolution for FMCW Radar in Autonomous Driving
We also propose a hybrid super-resolution model (Hybrid-SR) combining our ADC-SR with a standard RAD super-resolution model, and show that performance can be improved by a large margin.
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection
While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object perception.
Ranked #1 on
Robust Camera Only 3D Object Detection
on nuScenes-C
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection
While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced by multi-modal fusion.
Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection
To mitigate this problem, we propose the Self-Training Multimodal Vehicle Detection Network (ST-MVDNet) which leverages a Teacher-Student mutual learning framework and a simulated sensor noise model used in strong data augmentation for Lidar and Radar.
Flexible Networks for Learning Physical Dynamics of Deformable Objects
This precondition restrains the model from generalizing to real-world data, which is considered to be a sequence of unordered point sets.
Multi-Modality Task Cascade for 3D Object Detection
To provide a more integrated approach, we propose a novel Multi-Modality Task Cascade network (MTC-RCNN) that leverages 3D box proposals to improve 2D segmentation predictions, which are then used to further refine the 3D boxes.
