Search Results for author:
Found 8 papers, 6 papers with code
SPEAR: Receiver-to-Receiver Acoustic Neural Warping Field
We present SPEAR, a continuous receiver-to-receiver acoustic neural warping field for spatial acoustic effects prediction in an acoustic 3D space with a single stationary audio source.
Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models
Self-supervised depth estimation algorithms rely heavily on frame-warping relationships, exhibiting substantial performance degradation when applied in challenging circumstances, such as low-visibility and nighttime scenarios with varying illumination conditions.
Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation
We also introduce two margin-based losses for the point migration to enforce corrections for the false positives/negatives and cohesion of foreground points, significantly improving the performance.
Ranked #1 on
3D Instance Segmentation
on ScanNet(v2)
DynPoint: Dynamic Neural Point For View Synthesis
The introduction of neural radiance fields has greatly improved the effectiveness of view synthesis for monocular videos.
Tighter Variational Bounds are Not Necessarily Better. A Research Report on Implementation, Ablation Study, and Extensions
We provide theoretical and empirical evidence that increasing the number of importance samples $K$ in the importance weighted autoencoder (IWAE) (Burda et al., 2016) degrades the signal-to-noise ratio (SNR) of the gradient estimator in the inference network and thereby affecting the full learning process.
Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR
Supervised approaches typically require the annotation of large training sets; there has thus been great interest in leveraging weakly, semi- or self-supervised methods to avoid this, with much success.
When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimation
In this paper, we show how to use a combination of three techniques to allow the existing photometric losses to work for both day and nighttime images.
Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV
Monocular approaches to such tasks exist, and dense monocular mapping approaches have been successfully deployed for UAV applications.
Monocular 3D Human Pose Estimation
Monocular Depth Estimation
