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Found 12 papers, 0 papers with code
Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation
Estimating the pose of objects through vision is essential to make robotic platforms interact with the environment.
Re-Nerfing: Improving Novel Views Synthesis through Novel Views Synthesis
With Re-Nerfing, we enhance the geometric consistency of novel views as follows: First, we train a NeRF with the available views.
VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis
Creating high-quality view synthesis is essential for immersive applications but continues to be problematic, particularly in indoor environments and for real-time deployment.
3D Adversarial Augmentations for Robust Out-of-Domain Predictions
We conduct extensive experiments across a variety of scenarios on data from KITTI, Waymo, and CrashD for 3D object detection, and on data from SemanticKITTI, Waymo, and nuScenes for 3D semantic segmentation.
Robust Monocular Depth Estimation under Challenging Conditions
While state-of-the-art monocular depth estimation approaches achieve impressive results in ideal settings, they are highly unreliable under challenging illumination and weather conditions, such as at nighttime or in the presence of rain.
Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
By doing so, for the first time in panoptic segmentation with unknown objects, our U3HS is trained without unknown categories, reducing assumptions and leaving the settings as unconstrained as in real-life scenarios.
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection
Despite training only on a standard dataset, such as KITTI, augmenting with our vector fields significantly improves the generalization to differently shaped objects and scenes.
CertainNet: Sampling-free Uncertainty Estimation for Object Detection
Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings.
R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes
While self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches, violations of the static world assumption can still lead to erroneous depth predictions of traffic participants, posing a potential safety issue.
Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds
Panoptic segmentation has recently unified semantic and instance segmentation, previously addressed separately, thus taking a step further towards creating more comprehensive and efficient perception systems.
Signal Clustering with Class-independent Segmentation
Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches.
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements.
