CVPR 2024 论文列表

While large multimodal models (LMMs) have achieved remarkable progress generating pixel-level masks for image reasoning tasks involving multiple open-world targets remains a challenge. To bridge this gap we introduce PixelLM an effective and efficient LMM for pixel-level reasoning and understanding. Central to PixelLM are a novel lightweight pixel decoder and a comprehensive segmentation codebook. The decoder efficiently produces masks from the hidden embeddings of the codebook tokens which encode detailed target-relevant information. With this design PixelLM harmonizes with the structure of popular LMMs and avoids the need for additional costly segmentation models. Furthermore we propose a token fusion method to enhance the model's ability to differentiate between multiple targets leading to substantially improved mask quality. To advance research in this area we construct MUSE a high-quality multi-target reasoning segmentation benchmark. PixelLM excels across various pixel-level image reasoning and understanding tasks outperforming well-established methods in multiple benchmarks including MUSE and multi-referring segmentation. Comprehensive ablations confirm the efficacy of each proposed component. All code models and datasets will be publicly available.

Surgical decisions are informed by aligning rapid portable 2D intraoperative images (e.g. X-rays) to a high-fidelity 3D preoperative reference scan (e.g. CT). However 2D/3D registration can often fail in practice: conventional optimization methods are prohibitively slow and susceptible to local minima while neural networks trained on small datasets fail on new patients or require impractical landmark supervision. We present DiffPose a self-supervised approach that leverages patient-specific simulation and differentiable physics-based rendering to achieve accurate 2D/3D registration without relying on manually labeled data. Preoperatively a CNN is trained to regress the pose of a randomly oriented synthetic X-ray rendered from the preoperative CT. The CNN then initializes rapid intraoperative test-time optimization that uses the differentiable X-ray renderer to refine the solution. Our work further proposes several geometrically principled methods for sampling camera poses from SE(3) for sparse differentiable rendering and for driving registration in the tangent space se(3) with geodesic and multiscale locality-sensitive losses. DiffPose achieves sub-millimeter accuracy across surgical datasets at intraoperative speeds improving upon existing unsupervised methods by an order of magnitude and even outperforming supervised baselines. Our implementation is at https://github.com/eigenvivek/DiffPose.

In the film and gaming industries achieving a realistic hair appearance typically involves the use of strands originating from the scalp. However reconstructing these strands from observed surface images of hair presents significant challenges. The difficulty in acquiring Ground Truth (GT) data has led state-of-the-art learning-based methods to rely on pre-training with manually prepared synthetic CG data. This process is not only labor-intensive and costly but also introduces complications due to the domain gap when compared to real-world data. In this study we propose an optimization-based approach that eliminates the need for pre-training. Our method represents hair strands as line segments growing from the scalp and optimizes them using a novel differentiable rendering algorithm. To robustly optimize a substantial number of slender explicit geometries we introduce 3D orientation estimation utilizing global optimization strand initialization based on Laplace's equation and reparameterization that leverages geometric connectivity and spatial proximity. Unlike existing optimization-based methods our method is capable of reconstructing internal hair flow in an absolute direction. Our method exhibits robust and accurate inverse rendering surpassing the quality of existing methods and significantly improving processing speed.

Bokeh is widely used in photography to draw attention to the subject while effectively isolating distractions in the background. Computational methods can simulate bokeh effects without relying on a physical camera lens but the inaccurate lens modeling in existing filtering-based methods leads to artifacts that need post-processing or learning-based methods to fix. We propose Dr.Bokeh a novel rendering method that addresses the issue by directly correcting the defect that violates the physics in the current filtering-based bokeh rendering equation. Dr.Bokeh first preprocesses the input RGBD to obtain a layered scene representation. Dr.Bokeh then takes the layered representation and user-defined lens parameters to render photo-realistic lens blur based on the novel occlusion-aware bokeh rendering method. Experiments show that the non-learning based renderer Dr.Bokeh outperforms state-of-the-art bokeh rendering algorithms in terms of photo-realism. In addition extensive quantitative and qualitative evaluations show the more accurate lens model further pushes the limit of a closely related field depth-from-defocus.

We present differentiable point-based inverse rendering DPIR an analysis-by-synthesis method that processes images captured under diverse illuminations to estimate shape and spatially-varying BRDF. To this end we adopt point-based rendering eliminating the need for multiple samplings per ray typical of volumetric rendering thus significantly enhancing the speed of inverse rendering. To realize this idea we devise a hybrid point-volumetric representation for geometry and a regularized basis-BRDF representation for reflectance. The hybrid geometric representation enables fast rendering through point-based splatting while retaining the geometric details and stability inherent to SDF-based representations. The regularized basis-BRDF mitigates the ill-posedness of inverse rendering stemming from limited light-view angular samples. We also propose an efficient shadow detection method using point-based shadow map rendering. Our extensive evaluations demonstrate that DPIR outperforms prior works in terms of reconstruction accuracy computational efficiency and memory footprint. Furthermore our explicit point-based representation and rendering enables intuitive geometry and reflectance editing.

Micro-mesh (u-mesh) is a new graphics primitive for compact representation of extreme geometry consisting of a low-polygon base mesh enriched by per micro-vertex displacement. A new generation of GPUs supports this structure with hardware evolution on u-mesh ray tracing achieving real-time rendering in pixel level geometric details. In this article we present a differentiable framework to convert standard meshes into this efficient format offering a holistic scheme in contrast to the previous stage-based methods. In our construction context a u-mesh is defined where each base triangle is a parametric primitive which is then reparameterized with Laplacian operators for efficient geometry optimization. Our framework offers numerous advantages for high-quality u-mesh production: (i) end-to-end geometry optimization and displacement baking; (ii) enabling the differentiation of renderings with respect to umesh for faithful reprojectability; (iii) high scalability for integrating useful features for u-mesh production and rendering such as minimizing shell volume maintaining the isotropy of the base mesh and visual-guided adaptive level of detail. Extensive experiments on u-mesh construction for a large set of high-resolution meshes demonstrate the superior quality achieved by the proposed scheme.

Photometric stereo leverages variations in illumination conditions to reconstruct surface normals. Display photometric stereo which employs a conventional monitor as an illumination source has the potential to overcome limitations often encountered in bulky and difficult-to-use conventional setups. In this paper we present differentiable display photometric stereo (DDPS) addressing an often overlooked challenge in display photometric stereo: the design of display patterns. Departing from using heuristic display patterns DDPS learns the display patterns that yield accurate normal reconstruction for a target system in an end-to-end manner. To this end we propose a differentiable framework that couples basis-illumination image formation with analytic photometric-stereo reconstruction. The differentiable framework facilitates the effective learning of display patterns via auto-differentiation. Also for training supervision we propose to use 3D printing for creating a real-world training dataset enabling accurate reconstruction on the target real-world setup. Finally we exploit that conventional LCD monitors emit polarized light which allows for the optical separation of diffuse and specular reflections when combined with a polarization camera leading to accurate normal reconstruction. Extensive evaluation of DDPS shows improved normal-reconstruction accuracy compared to heuristic patterns and demonstrates compelling properties such as robustness to pattern initialization calibration errors and simplifications in image formation and reconstruction.

In recent years there has been a growing interest in training Neural Networks to approximate Unsigned Distance Fields (UDFs) for representing open surfaces in the context of 3D reconstruction. However UDFs are non-differentiable at the zero level set which leads to significant errors in distances and gradients generally resulting in fragmented and discontinuous surfaces. In this paper we propose to learn a hyperbolic scaling of the unsigned distance field which defines a new Eikonal problem with distinct boundary conditions. This allows our formulation to integrate seamlessly with state-of-the-art continuously differentiable implicit neural representation networks largely applied in the literature to represent signed distance fields. Our approach not only addresses the challenge of open surface representation but also demonstrates significant improvement in reconstruction quality and training performance. Moreover the unlocked field's differentiability allows the accurate computation of essential topological properties such as normal directions and curvatures pervasive in downstream tasks such as rendering. Through extensive experiments we validate our approach across various data sets and against competitive baselines. The results demonstrate enhanced accuracy and up to an order of magnitude increase in speed compared to previous methods.

In the evolving landscape of digital media and video production the precise manipulation and reproduction of visual elements like camera movements and character actions are highly desired. Existing SLAM methods face limitations in dynamic scenes and human pose estimation often focuses on 2D projections neglecting 3D statuses. To address these issues we first introduce a reverse filming behavior estimation technique. It optimizes camera trajectories by leveraging NeRF as a differentiable renderer and refining SMPL tracks. We then introduce a cinematic transfer pipeline that is able to transfer various shot types to a new 2D video or a 3D virtual environment. The incorporation of 3D engine workflow enables superior rendering and control abilities which also achieves a higher rating in the user study.