Search Results for author:
Found 16 papers, 5 papers with code
LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies
We assess 75 models using ImageNet as the ID dataset and five significantly shifted OOD variants, uncovering a strong linear correlation between ID LCA distance and OOD top-1 accuracy.
Better Call SAL: Towards Learning to Segment Anything in Lidar
We propose the SAL (Segment Anything in Lidar) method consisting of a text-promptable zero-shot model for segmenting and classifying any object in Lidar, and a pseudo-labeling engine that facilitates model training without manual supervision.
SeMoLi: What Moves Together Belongs Together
Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to pseudo-label instances of moving objects and use these as supervisory signals to train 3D object detectors in Lidar data without manual supervision.
Lidar Panoptic Segmentation and Tracking without Bells and Whistles
Our network is modular by design and optimized for all aspects of both the panoptic segmentation and tracking task.
Thinking Like an Annotator: Generation of Dataset Labeling Instructions
In Labeling Instruction Generation, we take a reasonably annotated dataset and: 1) generate a set of examples that are visually representative of each category in the dataset; 2) provide a text label that corresponds to each of the examples.
Fast Neural Scene Flow
Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with dense lidar points.
Ranked #5 on
Self-supervised Scene Flow Estimation
on Argoverse 2
Learning to Zoom and Unzoom
In this work (LZU), we "learn to zoom" in on the input image, compute spatial features, and then "unzoom" to revert any deformations.
SUDS: Scalable Urban Dynamic Scenes
We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes.
Priors are Powerful: Improving a Transformer for Multi-camera 3D Detection with 2D Priors
Transfomer-based approaches advance the recent development of multi-camera 3D detection both in academia and industry.
Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images
Moreover, we show that our retrieved maps can be used to update or expand existing maps and even show proof-of-concept results for visual localization and image retrieval from spatial graphs.
Far3Det: Towards Far-Field 3D Detection
We focus on the task of far-field 3D detection (Far3Det) of objects beyond a certain distance from an observer, e. g., $>$50m.
Flat Latent Manifolds for Human-machine Co-creation of Music
We therefore consider a music-generating algorithm as a counterpart to a human musician, in a setting where reciprocal interplay is to lead to new experiences, both for the musician and the audience.
Learning Flat Latent Manifolds with VAEs
Prevalent is the use of the Euclidean metric, which has the drawback of ignoring information about similarity of data stored in the decoder, as captured by the framework of Riemannian geometry.
FLAT MANIFOLD VAES
Latent-variable models represent observed data by mapping a prior distribution over some latent space to an observed space.
Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
We present a sampling-free approach for computing the epistemic uncertainty of a neural network.
Fast Approximate Geodesics for Deep Generative Models
The length of the geodesic between two data points along a Riemannian manifold, induced by a deep generative model, yields a principled measure of similarity.
