Instead of training asingle model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame.

Neural Information Processing Systems http://nips.cc/

A naiveimplementation of this approach leads to the dynamic component taking over the static one as the representation of the former is inherently more general and prone to overfitting.

In contrast, ViSER is able to correctlyreconstructthedancer.

MLPs with"noencoding" struggle tofit high frequencysegments (see appendix for train details). Our workenables MLP networks toadaptivelyfitavarying spectrum offine details that previous methods struggle to capture in a single shot, without involved tuning of parameters or domain specific preprocessing.

Neural fields have emerged as a new paradigm for representing signals, thanks to their ability to do it compactly while being easy to optimize. In most applications, however, neural fields are treated like black boxes, which precludes manysignal manipulation tasks.

Rounding out the story and contributions, firstly we present a brief toy univariate model hinting towards the necessity of early stopping: concretely, any univariate predictor satisfying alocal interpolation propertycan not achieve optimal test error for noisy distributions.

In the recent past, the seminal framework NeRF [19] inspired a lot of follow up work by modeling 3D objects as adensity functionσ(x)and view-dependent colorc(x,v)for each pointx R3 in the volume.

Visual SLAM in dynamic environments remains challenging, as several existing methods rely on semantic filtering that only handles known object classes, or use fixed robust kernels that cannot adapt to unknown moving objects, leading to degraded accuracy when they appear in the scene. We present VAR-SLAM (Visual Adaptive and Robust SLAM), an ORB-SLAM3-based system that combines a lightweight semantic keypoint filter to deal with known moving objects, with Barron's adaptive robust loss to handle unknown ones. The shape parameter of the robust kernel is estimated online from residuals, allowing the system to automatically adjust between Gaussian and heavy-tailed behavior. We evaluate VAR-SLAM on the TUM RGB-D, Bonn RGB-D Dynamic, and OpenLORIS datasets, which include both known and unknown moving objects. Results show improved trajectory accuracy and robustness over state-of-the-art baselines, achieving up to 25% lower ATE RMSE than NGD-SLAM on challenging sequences, while maintaining performance at 27 FPS on average.