UFV-Splatter: Pose-Free Feed-Forward 3D Gaussian Splatting Adapted to Unfavorable Views

This paper presents a pose-free, feed-forward 3D Gaussian Splatting (3DGS) framework designed to handle unfavorable input views. A common rendering setup for training feed-forward approaches places a 3D object at the world origin and renders it from cameras pointed toward the origin -- i.e., from favorable views, limiting the applicability of these models to real-world scenarios involving varying and unknown camera poses. To overcome this limitation, we introduce a novel adaptation framework that enables pretrained pose-free feed-forward 3DGS models to handle unfavorable views. We leverage priors learned from favorable images by feeding recentered images into a pretrained model augmented with low-rank adaptation (LoRA) layers. We further propose a Gaussian adapter module to enhance the geometric consistency of the Gaussians derived from the recentered inputs, along with a Gaussian alignment method to render accurate target views for training. Additionally, we introduce a new training strategy that utilizes an off-the-shelf dataset composed solely of favorable images. Experimental results on both synthetic images from the Google Scanned Objects dataset and real images from the OmniObject3D dataset validate the effectiveness of our method in handling unfavorable input views.

NLOS-NeuS: Non-line-of-sight Neural Implicit Surface

Non-line-of-sight (NLOS) imaging is conducted to infer invisible scenes from indirect light on visible objects. The neural transient field (NeTF) was proposed for representing scenes as neural radiance fields in NLOS scenes. We propose NLOS neural implicit surface (NLOS-NeuS), which extends the NeTF to neural implicit surfaces with a signed distance function (SDF) for reconstructing three-dimensional surfaces in NLOS scenes. We introduce two constraints as loss functions for correctly learning an SDF to avoid non-zero level-set surfaces. We also introduce a lower bound constraint of an SDF based on the geometry of the first-returning photons. The experimental results indicate that these constraints are essential for learning a correct SDF in NLOS scenes. Compared with previous methods with discretized representation, NLOS-NeuS with the neural continuous representation enables us to reconstruct smooth surfaces while preserving fine details in NLOS scenes. To the best of our knowledge, this is the first study on neural implicit surfaces with volume rendering in NLOS scenes.

Deep Depth from Focal Stack with Defocus Model for Camera-Setting Invariance

We propose a learning-based depth from focus/defocus (DFF), which takes a focal stack as input for estimating scene depth. Defocus blur is a useful cue for depth estimation. However, the size of the blur depends on not only scene depth but also camera settings such as focus distance, focal length, and f-number. Current learning-based methods without any defocus models cannot estimate a correct depth map if camera settings are different at training and test times. Our method takes a plane sweep volume as input for the constraint between scene depth, defocus images, and camera settings, and this intermediate representation enables depth estimation with different camera settings at training and test times. This camera-setting invariance can enhance the applicability of learning-based DFF methods. The experimental results also indicate that our method is robust against a synthetic-to-real domain gap, and exhibits state-of-the-art performance.

Registration of serial sections: An evaluation method based on distortions of the ground truths

Registration of histological serial sections is a challenging task. Serial sections exhibit distortions from sectioning. Missing information on how the tissue looked before cutting makes a realistic validation of 2D registrations impossible. This work proposes methods for more realistic evaluation of registrations. Firstly, we survey existing registration and validation efforts. Secondly, we present a methodology to generate test data for registrations. We distort an innately registered image stack in the manner similar to the cutting distortion of serial sections. Test cases are generated from existing 3D data sets, thus the ground truth is known. Thirdly, our test case generation premises evaluation of the registrations with known ground truths. Our methodology for such an evaluation technique distinguishes this work from other approaches. We present a full-series evaluation across six different registration methods applied to our distorted 3D data sets of animal lungs. Our distorted and ground truth data sets are made publicly available.